Kỹ năng phát hiện vật thể (người) bằng phương pháp trích đặc trưng ảnh HOG và phân lớp sử dụng SVM (Support Vector Machine)

7/31/2019 K nng pht hin vt th (ngi) bng phng php trch c trng nh HOG v phn lp s dng SVM (Support

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TRNG I HC KHOA HC TNHIN

KHOA CNG NGH

THNG TIN

LP CNHN TI NNG

Nguyn Xun Tng Huy 0612128

Nguyn Hong V 0612573

TM HIU KTHUT PHT HIN NGITRONG VIDEO GIM ST LP HC

KHA LUN TT NGHIP CNHN CNTT

GIO VIN HNG DN

TS. inh B Tin

KHA 2006 - 2010


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i

NHN XT CA GIO VIN HNG DN

TpHCM, ngy .. thng nm

Gio vin hng dn


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ii

NHN XT CA GIO VIN PHN BIN

Kha lun p ng yu cu ca Kha lun c nhn CNTT.

TpHCM, ngy .. thng nm

Gio vin phn bin


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iii

LI CM N

Trc tin, chng em xin gi li cm n su sc n Thy inh B Tin v

Thy inh B Thng tn tnh hng dn, ch bo chng em trong sut thi gian

thc hin ti. Nu khng c c nhng s ch bo ca cc thy, lun vn ca

chng em s rt kh hon thnh. Qua thi gian c cc thy hng dn, chng em

hc hi c nhiu kin thc b ch v kinh nghim qu bu lm nn tng cho

qu trnh hc tp, lm vic v nghin cu sau ny.

Chng em xin chn thnh cm n anh L Phong, ngi tn ty ch bo v

theo st nhm em trong sut qu trnh lm ti.

Chng em xin trn trng cm n qu Thy c trong Khoa Cng ngh thng

tin trng i hc Khoa hc Tnhin TP H Ch Minh tn tnh ging dy,

truyn t nhng kin thc qu bu trong sut bn nm hc lm nn tng v to

iu kin cho chng em c thc hin lun vn ny.

Chng con xin c by t lng bit n su sc i vi ng B, Cha M,

nhng ngi lun ng vin, chm sc c v tinh thn ln vt cht, lun to iu

kin cho chng con chuyn tm hc tp v nghin cu.

Chng mnh xin trn trng cm n cc bn trong nhm tch cc h tr

chng mnh hon thnh c lun vn ny.

Mc d rt c gng n lc ht mnh, song chc chn lun vn khng khi

cn nhiu thiu st. Chng em rt mong nhn c s thng cm v ch bo tn tnhca qu Thy c v cc bn.

TP. H Ch Minh , 07/2010

Chn Thnh Cm n

Nhm Sinh Vin Thc Hin


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CNG CHI TIT

Tn Ti: Tm hiu k thut pht hin ngi trong video gim st lphc.

Gio vin hng dn: TS. inh B Tin.

Thi gian thc hin: t ngy 12/12/2009 n ngy 07/07/2010.

Sinh vin thc hin:

Nguyn Xun Tng Huy (0612128)Nguyn Hong V (0612573).

Loi ti: Nghin cu v xy dng cc phng php pht hin i tng

Ni Dung Ti (m t chi tit ni dung ti, yu cu, phng php

thc hin, kt qut c, ):

y l ti thuc vhng nghin cu cc k thut pht hin ngi trong

mi trng lp hc v pht trin ng dng thc nghim. ti bao gm cc phn

sau: Nghin cu v tm hiu cc phng php tr nn c.

Nghin cu v tm hiu cc phng php pht hin mu.

Nghin cu v tm hiu pht hin theo dng mu snake.

Nghin cu v tm hiu cch rt trch c trng HOG.

Nghin cu v tm hiu phng php my hc SVM.

Xy dng h thng pht hin ngi trong lp hc minh ha.

K Hoch Thc Hin:

12/12/2009 - 26/01/2010: Tm hiu cc phng php tr nn sn c.

27/01/2010 - 26/2/2010: Tm hiu cc phng php theo vt i tng theo


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v

hng tip cn Filtering.

27/02/2010 - 15/03/2010: Tm hiu cc phng php pht hin mu v ng

dng vo video lp hc.

15/03/2010 - 14/04/2010: Tm hiu vc trng HOG cng vi SVM.

15/04/2010 - 1/05/2010: Tm hiu vphng php pht hin theo dng mu.

1/05/2010 - 01/06/2010: Tch hp ton h thng.

02/06/2010 - 30/06/2010: Ci tin cc phng php s dng.

01/07/2010 - 15/07/20010: Xy dng chng trnh minh ha.

Xc nhn ca GVHD Ngy 15 thng 7 nm 2010

SV Thc hin


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Mc lc

LI CM N ................................................................................................ iiiCNG CHI TIT ................................................................................. ivMc lc........................................................................................................... viDanh mc hnh ................................................................................................. xDanh mc bng ............................................................................................ xivTm tt kha lun .......................................................................................... xvPhn mu ..................................................................................................... 1Gii thiu bi ton pht hin i tng ....................................................... 1Pht hin hc sinh trong lp hc .................................................................. 1B cc .............................................................................................................. 4Phn A: Vn v bi ton pht hin i tng ............................................. 6Chng 1. Cc phng php tr nn (Background subtraction) ............... 10

1.1 Gii thiu .......................................................................................... 101.2 Cc phng php c bn .................................................................. 10

1.2.1 Frame Differencing ..................................................................... 101.2.2 Running Average ........................................................................ 131.2.3 Running Median ......................................................................... 15

1.3 Cc phng php cao cp ................................................................. 171.3.1 Running Gaussian Average ........................................................ 171.3.2 Codebook .................................................................................... 18

Chng 2. Cc phng php pht hin mu ............................................... 242.1 Pht hin mu bng phng php c bn......................................... 24


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2.2 Pht hin mu bng m hnh Gauss ................................................. 242.2.1 Single Gaussian .......................................................................... 242.2.2 Mixture of Gaussians .................................................................. 27

2.3 Kt lun ............................................................................................ 28Chng 3. Pht hin theo dng mu snake .................................................. 30

3.1 Gii thiu .......................................................................................... 303.2 Biu din ........................................................................................... 303.3 Khng gian trng thi ....................................................................... 323.4 So khp ............................................................................................. 35

Chng 4. Histogram of gradient (HOG) .................................................... 374.1 Gii thiu .......................................................................................... 374.2 Rt trch c trng HOG trong nh .................................................. 384.3 Chun ha vector c trng cho tng block ..................................... 424.4 Integral Image ................................................................................... 424.5 Cng thc quy tnh Integral Image .............................................. 44

Chng 5. Support Vector Machines (SVM) .............................................. 455.1 Gii thiu .......................................................................................... 455.2 Cc khi nim c bn ....................................................................... 45

5.2.1 Siu phng phn cch ................................................................. 455.2.2 Support vectors ........................................................................... 495.2.3 Bin (Margin) ........................................................................ 51

5.3 Phn lp d liu ................................................................................ 515.3.1 Trng hp d liu c th phn chia tuyn tnh c ............... 525.3.2 Trng hp d liu khng th phn chia tuyn tnh c ......... 52


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5.4 Nhn xt............................................................................................ 54Phn B: ng dng bi ton pht hin i tng trong lp hc ..................... 55Chng 6. Gii thiu bi ton ..................................................................... 55

6.1 Gii thiu v h thng pht hin hc sinh trong lp hc ................. 556.2 Mt s gi thit t ra trong ti.................................................... 576.3 M t d liu video lp hc ............................................................. 59

6.3.1 Mc ch s dng ....................................................................... 596.3.2 Chi tit v b d liu .................................................................. 59

Chng 7. Pht hin mu tc trong video lp hc ...................................... 647.1 Gii thiu bi ton ............................................................................ 647.2 Phng php .................................................................................... 647.3 Nhn xt............................................................................................ 66

Chng 8. p dng c trng HOG cng vi SVM trong vic pht hinhc sinh trong lp hc ............................................................................................... 68

8.1 Gii thiu bi ton ............................................................................ 688.2 Hun luyn cho b phn lp SVM ................................................... 708.3 Tin hnh phn lp ........................................................................... 728.4 Kt qu thc nghim ........................................................................ 748.5 Nhn xt............................................................................................ 76

Chng 9. p dng Snake trong vic pht hin hc sinh trong lp hc ..... 789.1 Gii thiu .......................................................................................... 789.2 Xy dng d liu .............................................................................. 789.3 Nhn xt............................................................................................ 80

Chng 10. Kt lun v hng pht trin ................................................... 81


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10.1 Kt lun ............................................................................................ 8110.2 Hng pht trin ............................................................................... 81

Ti Liu Tham Kho ...................................................................................... 82


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Danh mc hnh

Hnh 1. Minh ha cc hc sinh ni chuyn trong lp ...................................... 2Hnh 2. Minh ha hc sinh ng gc trong lp ................................................. 2Hnh 3. nh sng khng trong video .......................................................... 3Hnh 4. nh sng qu mnh trong video ......................................................... 4Hnh 5: S tng quan v vn pht hin i tng bng cc phng

php my hc [4] ......................................................................................................... 8Hnh 6: Kt qu khi thc hin thut ton frame differencing trn video VN-

SIN-VE_02.avi. i tng c khoang trn trn hnh cho ta thy i vi nhngim bn trong lng i tng b xem nhm l background .................................... 12

Hnh 7: Kt qu khi thc hin thut ton frame differencing trn video

Leavy.avi. i tng c khoang trn trn hnh do ng yn nn thut ton xem

nhm l background .................................................................................................. 13Hnh 8: Kt qu khi thc hin thut ton Running Average trn video

Leavy.avi ................................................................................................................... 15Hnh 9: Kt qu chy thut ton khng tt i vi nhng video c

background thay i .................................................................................................. 15Hnh 10: Kt qu khi thc hin thut ton Running Average trn video VN-

SIN-VE_02.avi .......................................................................................................... 17Hnh 11: M hnh codeword [8] .................................................................... 19Hnh 12: Kt qu khi thc hin thut ton Codebook trn video VN-SIN-

VE_02.avi .................................................................................................................. 23Hnh 13. Kt qu lc mu tc da trn h mu RGB ca video

3_13.45.00.avi ........................................................................................................... 24Hnh 14: B d liu mu tc ca ngi ......................................................... 26Hnh 15: Kt qu cho vic phn lp mu s dng m hnh Gauss trn video

4.avi. Mu ta mong mun pht hin y l mu tc ca ngi ............................. 27


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Hnh 16: Kt qu cho vic phn lp mu tc trn video 3_14.10.00.avi. Mu

ta mong mun pht hin y l mu tc ca ngi ............................................... 27Hnh 17. Minh ha so khp snake trn nh ................................................... 30Hnh 18. Minh ha vic xy dng snake t tp template point c sn: nhng

im mu trng l template point, nhng im mu xanh l control point .............. 32Hnh 19. Su mc bin i affine c bn....................................................... 33Hnh 20. Minh ha p dng php bin i affine trn ng cong ............... 35Hnh 21. Minh ha so khp khng gian snake vi bin tm c trn nh:

ng mu l template, ng mu vng l ng cong c kht cao nht .... 36Hnh 22: R-HOG v C-HOG [10] .................................................................. 37Hnh 23: Mi khi (block) gm nhiu (cell). V d trong hnh trn ta thy

mt khi gm 9 [10] ............................................................................................... 38Hnh 24: Cc khi c xp chng ln nhau [10] ......................................... 38Hnh 25 tnh gc v bin theo X-gradient v Y-gradient.......................... 40Hnh 26: Cc bc rt trch c trng HOG [4] ............................................ 41Hnh 27 Integral Image [11]........................................................................... 43Hnh 28 Cng thc tnh integral image [12] .................................................. 43Hnh 29: ng mu vng trn hnh l ng phn chia i vi tp d liu

gm hai thuc tnh [15] ............................................................................................. 46Hnh 30: Mt b d liu hai chiu c phn chia tuyn tnh. C v hn

ng thng phn chia tuyn tnh c th . Vy ci no l tt nht? [14] .................. 47Hnh 31: Trong hnh ny chng ta c 2 siu phng phn chia tuyn tnh cng

vi bin ca n. Ci no tt hn? Ci c bin ln hn th sc chnh xc

cao hn [14] .............................................................................................................. 48Hnh 32: ng biu din H1 v H2, ng mu l khong cch

Euclidean ca hai im 1 v 2. ng mu xanh l khong cch Euclidean nh nht

[15] ............................................................................................................................ 50Hnh 33: Cc support vector trong SVM. Cc support vector l nhng hnh

c vin dy hn. [14] ................................................................................................. 51


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Hnh 34: Mt trng hp n gin trn khng gian 2 chiu. Khc vi d liu

c th phn chia tuyn tnh c trn hnh 6.20, y ta khng th v mt ng

thng phn chia 2 lp. Do vy ranh gii quyt nh y th khng tuyn tnh. [14]

................................................................................................................................... 53Hnh 35: M hnh chung ca h thng pht hin hc sinh trong lp ............. 55Hnh 36: Kt qut c sau khi thc hin vic pht hin mu tc ........... 56Hnh 37: Kt qu sau khi pht hin u ngi bng SVM v HOG ............. 57Hnh 38: Mt hnh minh ha trong video 2.avi ............................................. 58Hnh 39: Khu vc gii hn x l ca h thng. Vng trng l vng khng x

l ................................................................................................................................ 58Hnh 40: Mt i tng khng nhn r trong video 3.avi .............................. 59Hnh 41: Mt skh khn trong video 2.avi ................................................. 61Hnh 42: Kh khn gp phi trong video 3.avi .............................................. 62Hnh 43: Kh khn trong video 1.avi............................................................. 63Hnh 44: B d liu mu tc ca ngi ......................................................... 64Hnh 45: Knh mu HSV [16] ....................................................................... 65Hnh 46: Kt qu cho vic phn lp mu tc s dng m hnh Gauss trn

video 4.avi ................................................................................................................. 66Hnh 47: Kt qu trong vic tm cc vng hnh ch nht bao cc mu tc ... 66Hnh 48: Kt qu trong vic pht hin mu tc chng 7 .......................... 68Hnh 49: Kt quthu c khi loi i nhng vng c din tch qu nh hoc

qu ln ....................................................................................................................... 69Hnh 50: Qu trnh pht hin i tng bng SVM v c trng HOG [4] .. 70Hnh 51: Mt s hnh nh trong b positive .................................................. 70Hnh 52: Mt hnh bt k trong tp negative ................................................. 71Hnh 53: Mrng hnh ch nht bao vng mu tc ...................................... 72Hnh 54: Pht hin phn thn trn ca ngi ti v tr mu tc v nhng v tr

xung quanh ................................................................................................................ 73


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Hnh 55: Kt hp cc hnh ch nht li vi nhau. Hnh ch nht mu xanh l

kt qu ca vic kt hp cc hnh ch nht .......................................................... 74Hnh 56: Kt qu cui cng ca vic pht hin phn trn ca ngi ............ 74Hnh 57: Kt qu khi chy vi video 3.avi .................................................... 76Hnh 58: Trng hp pht hin mu tc khng chnh xc do mu en ca tc

trng vi mu en ca laptop v mu o .................................................................. 77Hnh 59: Trng hp mu tc dnh vi mu o ............................................ 77Hnh 60: Trng hp hai u chm li vi nhau ........................................... 77Hnh 61. Cc trng hp c th xy ra nhm ln sau khi p dng b phn lp

SVM .......................................................................................................................... 78Hnh 62. Mt s dng mu omega c dng so khp ............................ 79Hnh 63. Kt qu sau khi s dng dng mu omega kim tra cc vng u

vai ngi trong nh, do nhiu khin kt qu khng tt ............................................ 79Hnh 64. Cc dng mu na vng cung c dng so khp .................... 79Hnh 65. Kt qu khi so khp cc dng mu na vng cung vi cc phn u

ca ngi c pht hin ..................................................................................... 80


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Danh mc bng

Bng 1: Bng phn loi cc thut ton pht hin i tng [1]....................... 6

Bng 2: Bng kt qu thc nghim khi chy thut ton frame differencing . 12Bng 3: Bng kt qu thc nghim khi chy thut ton Running Average ... 14Bng 4: Bng kt qu thc nghim khi chy thut ton Running Median .... 17Bng 5: Bng kt qu thc nghim khi chy thut ton Running Gaussian

Average ..................................................................................................................... 18Bng 6: Bng kt qu thc nghim khi chy thut ton Codebook ............... 22Bng 7: Bng thng k tp video d liu ....................................................... 60

Bng 8: Kt qu ca vic pht hin i tng bng phng php phn lp

SVM i vi b d liu ............................................................................................. 75


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Tm tt kha lun

Pht hin i tng (object detection) l mt bi ton quan trng trong lnh

vc th gic my tnh. y l mt hng nghin cu c nhiu ng dng trong thc

tnh th thao, giao thng, giao tip ngi v my, lp hc Kha lun ny tp

trung nghin cu cc vn v pht hin i tng v ng dng ch yu vo lnh

vc pht hin hc sinh trong video gim st lp hc. Kha lun ny c thc hin

vi cc mc ch:

Tm hiu cc phng php pht hin i tng khc nhau.

ng dng cc thut ton pht hin i tng vo thc tin, c bit l

lnh vc gim st lp hc.

Cc ni dung chnh c trnh by trong kha lun bao gm:

Tm hiu cc thut ton tr mu nn (Background Subtraction).

Tm hiu cc thut ton pht hin mu.

Tm hiu cch rt trch c trng HOG v p dng phng php my

hc SVM trong vic pht hin i tng.

Tm hiu cch pht hin i tng theo dng mu snake.

Xy dng ng dng minh ha trong lnh vc gim st lp hc.

T kha: pht hin i tng (object detection), pht hin phn thn trn

ca ngi (upper body detection).


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Phn mu

Gii thiu bi ton pht hin i tng

Pht hin i tng trong th gic my tnh l cng vic tm kim cc vt th

trong nh hoc trong video. y l hng nghin cu c quan tm nhiu hin

nay vi nhiu ng dng. Mt nhnh trong pht hin i tng l pht hin ngi

trong nh hoc video. Con ngi c th nhn bit cc i tng l ngi mt cch

d dng trong cc nh hoc video d s khc nhau l rt a dng. Nhng i vi

h thng th gic my tnh th cng vic ny vn cn l thch thc ln. Nguyn

nhn ch yu l do din mo v t th ca mi ngi l ht sc a dng, nh

hng v iu kin nh sng, s che lp ln nhau ca nhng ngi trong m

ng, cht lng video

tng chnh yu trong cc phng php pht hin i tng l da vo

c trng c thc rt trch ra t nhng i tng mu. c trng ny c s

dng cng vi mt b phn lp hoc s dng thut ton pht hin ra nhng i

tng tng t vi cc i tng mu

Pht hin hc sinh trong lp hc

Nhn dng hc sinh trong lp hc l bi ton c nhiu ng dng trong thc

t. Vic pht hin tt, chnh xc cc hc sinh trong lp hc s to tin cho rt

nhiu hng pht trin nh:

Theo vt cc hc sinh trong lp. Pht hin ra nhng hnh vi, c ch trong lp nh: pht biu trong gi

hc, ni chuyn ring, ng gt

Qun l lp hc nh im danh, m slng sinh vin c trong lp.

nh gi trng thi ca lp hc nh: mt lp hc l ng hay

tha? dao ng ca cc hc sinh trong lp nh th no


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Hnh 1. Minh ha cc hc sinh ni chuyn trong lp

Hnh 2. Minh ha hc sinh ng gc trong lp

Kh khn thng xuyn phi i mt trong ti l:


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Cht lng cc video gim st lp hc thng khng cao.

Kh khn gp phi viu kin nh sng

Kh khn v s che lp thng xuyn ca nhng dy hc sinh

Hnh 3. nh sng khng trong video


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Hnh 4. nh sng qu mnh trong video

B cc

Ni dung ca kha lun c trnh by trong 10 chng, bao gm cc ni

dung vcc phng php v vn v pht hin i tng. Cng vi vic p dng

cc phng php ny vo vn pht hin ngi trong video gim st lp hc.

Chng 1 Cc phng php trnn (Background subtraction): Gii

thiu cch pht hin cc i tng chuyn ng bng nhng phng php tr nn

c bn v nng cao.

Chng 2 Cc phng php pht hin mu: Gii thiu v cc cch pht

hin mu sc.

Chng 3 Pht hin theo dng mu snake: Gii thiu snake, cch xy

dng khng gian dng mu v so khp vi i tng.


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Chng 4 Histogram of gradient (HOG): Gii thiu tng quan vc

trng HOG cng vi cch rt trch c trng HOG t mt nh.

Chng 5Support Vector Machines (SVM): Gii thiu tng quan v bphn lp SVM.

Chng 6 Gii thiu bi ton: Gii thiu v bi ton pht hin ngi

trong video gim st lp hc, gii thiu m hnh chung ca h thng pht hin

ngi, v mt s ginh ca bi ton. M t b d liu video lp hc.

Chng 7Pht hin mu tc trong video lp hc: Gii thiu v cch p

dng m hnh Single Gauss pht hin mu tc ngi.

Chng 8 p dng c trng HOG cng vi SVM trong vic pht

hin hc sinh trong lp hc: M t qu trnh ng dng b phn lp SVM cng vi

c trng HOG pht hin ngi trong video lp hc.

Chng 9 Tch hp: Chng cui cng c ti, tm li cc vn

gii quyt v cc hng pht trin trong tng lai.

Chng 11 Kt lun v hng pht trin: Chng cui cng c ti,

tm li cc vn gii quyt v cc hng pht trin trong tng lai.


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Phn A:Vn v bi ton pht hin i tng

Vn pht hin i tng l lnh vc c nghin cu rng ri v c nhiu

ng dng trong cuc sng hin nay. Phn ln cc i tng c pht hin qua vic

da vo nhng thng tin trong mt frame nh. C rt nhiu hng tip cn gii

quyt vn trn. Cc hng tip cn ny c phn thnh nhiu loi nh sau [1]:

Loi Nhng nghin cu lin quan

Point detectors

Moravecs detector

Harris detector

Scale Invariant Feature Transform

Affine Invariant Point Detector

Segmentation

Mean-shift

Graph-cut

Active contours

Background Modeling

Mixture of Gaussians

Eigenbackground

Wall flower

Dynamic texture background

Supervised Classifier

Support Vector Machines

Neural Networks

Adaptive Boosting

Bng 1: Bng phn loi cc thut ton pht hin i tng [1]

Qua bng trn ta thy c nhiu hng tip cn x l vn pht hin i

tng. Tuy nhin vic la chn phng php p dng th da vo tnh hung c th.


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i vi trng hp c nh nn khng thay i th vic pht hin i tng

chuyn ng c thc thc hin bng cc phng php tr nn. Chi tit v cc

gii thut ny sc trnh by chng sau.Hng gii quyt thng gp l xy

dng m hnh nn, sau s dng m hnh ny cng vi frame hin ti t rt

ra c cc foreground chuyn ng. u im ca gii thut ny l n gin. c

th tip cn theo hng ny th ta cn phi xy dng c m hnh background. C

rt nhiu phng php xy dng m hnh background nh: Anurag Mittal dng

adaptive kernel density estimation xy dng m hnh nn [2]. Phng php ny

cho kt qu tt tuy nhin gp kh khn vkhng gian lu tr, tnh ton phc tp,

tc khng p ng thi gian thc. Haritaoglu dng gii thut W4, Stauffer sdng Mixture of Gaussian [3] xy dng m hnh nn Sau khi pht hin ra

c cc i tng chuyn ng th vic xc nh xem nhng i tng ny c

ng l nhng i tng ta cn pht hin hay khng cng l mt kh khn ln phi

i mt.

Trong cc lnh vc v pht hin phn u ca ngi th Wei Qu, Nidhal

Bouaynaya and Dan Schonfeld ra hng tip cn bng cch kt hp m hnh

mu da cng vi m hnh mu tc (skin and hair color model). Nhng mu ny

c pht hin da vo m hnh Gauss. Sau bng cch p dngphng php so

khp mu (template matching) t c mc ch pht hin phn u ngi

p ng thi gian thc. Kh khn trong hng tip cn ny thng gp vic thu

thp d liu hun luyn mu da v mu tc, chnh xc d bnh hng bi

sng ca mi trng.

Vic pht hin i tng c thc thc hin bng cc phng php my

hc. Cc phng php ny c th kn nh: mng neural, adaptive boosting, cy

quyt nh, support vector machines. im chung ca cc phng php ny u

phi tri qua giai on hun luyn trn mt tp d liu. Tp d liu ny phi ln,

bao qut ht c cc trng thi ca i tng. Sau cc c trng sc rt

trch ra trn b d liu hun luyn ny. Vic la chn c trng s dng ng vai


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tr quan trng nh hng n hiu qu ca cc phng php my hc. Mt sc

trng thng c s dng nh: c trng v mu sc, c trng v gc cnh, c

trng histogram Sau khi c c c trng, ta snh nhn lp c th cho cc

c trng s dng trong vic hun luyn. Trong qu trnh hun luyn, cc

phng php my hc s sinh ra mt hm nh x nhng c trngu vo tng

ng vi nhn lp c th. Sau khi hun luyn xong th cc phng php my hc

trn s c dng phn lp cho nhng c trng mi. c im ca phng

php ny l chnh xc cao. Tuy nhin n gp phi kh khn trong vic thu thp

d liu hun luyn ban u, tn thi gian v chi ph cho qu trnh hc my.

Hnh 5: S tng quan v vn pht hin i tng bng cc phng php my hc [4]

i vi vn v pht hin hc sinh trong lp hc, do gp phi kh khn

trong vic p dng phng php tr nn v t l foreground trong frame nh l kh

ln, hn na mu sc a dng, nh sng v vtr cc vt trong phng hc c th

thay i v cc i tng hc sinh di chuyn khng nhiu nn d dng b cc

phng php trn nhn nhm l phn nn... Vic pht hin u ngi bng cch da

vo mu da v mu tc th khng cho kt qu tt. Do mu da gn ging vi mu

bn gh, mu tc gn ging vi mu ca... V thnhm em xut ra hng tip

cn trong vic pht hin phn u ca ngi l da vo c trng hnh dng cng

vi phng php phn loi bng my hc. c th pht hin c tt phn u

ngi trong lp hc th nhm em tip cn vn qua hai bc:

Bc 1: Tin x l, c lng v tr xut hin ca u ngi da vo mu

tc.


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Bc 2: s dng thng tin v v tr ca bc trn pht hin u ngi

bng hai phng php chnh l pht hin theo dng mu snake v s dng

c trng HOG cng vi SVM.


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Chng 1. Cc phng php tr nn (Background subtraction)1.1 Gii thiu

Trong nhng ng dng v th gic my tnh, vn c bn nht c t ra

l lm th no pht hin c cc i tng chuyn ng trong video

(foreground), lm th no bit c u l nhng cnh vt khng thay i

(background) gii quyt c nhng vn nu trn th ta dng phng

php tr nn. y l mt trong nhng phng php nn tng nht trong lnh vc th

gic my tnh. C nhiu gii thut khc nhau v tr nn. Cc gii thut ny c

chia thnh hai nhm l: phng php c bn vphng php cao cp. Trong gii

hn ca lun vn ny, chng em s trnh by chi tit v nhng gii thut tr nn sauy:

Phng php cbn:

o Frame Differencing

o Running Average

o Running Median

Phng php cao cp:o Running Gaussian Average

o Codebook

tng chung ca cc phng php tr nn: pht hin ra c cc i

tng chuyn ng trong video chng ta phi c c m hnh background. M

hnh background ny c thc hc qua nhiu frame nh. Sau ta s dng m

hnh background ny so snh vi frame nh hin ti v kt qu l ta s nhn bitc u l vng nn, u l cc i tng.

1.2 Cc phng php c bn1.2.1 Frame DifferencingTrong tt c cc phng php background subtraction th phng php

Frame Differencing c xem l phng php n gin nht. Chi ph tnh ton


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thp, tc thc thi thut ton nhanh. Tuy nhin kt qut c khi ta p dng

thut ton ny l tng i thp.

tng chnh trong phng php Frame Differencing: cc i tng chuynng sc pht hin da trn s khc bit gia hai frame nh lin tip nhau cng

vi mt ngng c chn trc. Gii thut trn c thc hin bng phng php

tr hai frame lin tip, i vi mi gi tr pixel kt qu ta so snh gi tr ti pixel

vi ngng c chn. Nu gi tr ny nm trong ngng cho php th ti ta

xem nh l background. Ngc li l foreground. [5]

| | (1.1)Trong : Ft+1: frame nh tht - 1

Ft: frame nh tht

th: ngng c chn. chnh xc ca thut ton ph thuc vo gi tr

ngng ny.

u im:

Dci t.

Tc thc thi nhanh.

Thut ton chy chnh xc trong trng hp cc i tng di chuyn lin tc,

nn background l tnh.

Khuyt im:

chnh xc ca thut ton l tng i thp.

Thut ton trn chxc nh foregound chnh xc i vi nhng im bin

ca i tng. i vi nhng im nhbn trong lng i tng do s dch

chuyn ca nhng im nh ny l khng ng kdo s bxem nh l

background.


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Trong trng hp khi mt i tng ngng chuyn ng trong mt khong

thi gian th s b xem l background.

Kt qu thc nghim: Cu hnh my Pentium Dual Core, 2.5 GHz, 2GB RAM.

Tn video Kch thc frame Tng s frameTc xl

(fps)

VN-SIN-VE_02.avi 352 x 288 758 33

Leavy.avi 360 x 240 4362 31

Bng 2: Bng kt qu thc nghim khi chy thut ton frame differencing

Hnh 6: Kt qu khi thc hin thut ton frame differencing trn video VN-SIN-VE_02.avi.i tng c khoang trn trn hnh cho ta thy i vi nhng im bn trong lng i

tng b xem nhm l background


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Hnh 7: Kt qu khi thc hin thut ton frame differencing trn video Leavy.avi. i tngc khoang trn trn hnh do ng yn nn thut ton xem nhm l background

1.2.2 Running AverageTrong phng php frame differencing cp trn. Do vic xc nh

cc i tng ch da vo s khc bit hai frame lin tip nhau. Do vy chnh

xc ca thut ton tng i thp. khc phc nhc im ny th phng php

Running Average ra hng gii quyt l xy dng nn m hnh background. Sau

m hnh background ny c s dng tm ra foreground. M hnh

background ny c xy dng bng cch hc qua n frame lin tip. Gi tr pixel tiv tr (x,y) ca m hnh background ny c tnh bng cch ly trung bnh cng

ca tt c cc gi tr pixel ti v tr(x,y) ca nframe hc. [6]

(1.2)Trong :

B(x, y): gi tr pixel ti v tr(x, y) ca m hnh background

Fi(x, y): gi tr pixel ti v tr(x, y) ca frame thi

n: tng s frame hc

Sau khi xy dng c m hnh background, vic xc nh ra c u l

i tng foreground c thc hin bng cch so snh tng frame nh vi m hnh


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background. Da vo gi tr ca ngng c chn trc tng tnh phng

php frame differencing.

| | (1.3) chnh xc ca thut ton ph thuc vo m hnh background. Do ci tin thut ton tt hn ta cp nht m hnh background qua tng bc chy thut

ton.

(1.4)

thng c chn l 0.05.

Nh vy, m hnh background sc cp nht lin tc sau mi frame. V

vy sgip tng chnh xc ca phng php.

u im:

chnh xc cao hn phng php frame differencing.

Khuyt im:

Tn chi ph trong khi hc ra m hnh background

Thut ton ch p dng tt i vi trng hp background tnh khng thay

i


Tn video Kch thc frame Tng s frame Tc xl(fps)

VN-SIN-VE_02.avi 352 x 288 758 57

Leavy.avi 360 x 240 4362 30

Bng 3: Bng kt qu thc nghim khi chy thut ton Running Average


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Hnh 8: Kt qu khi thc hin thut ton Running Average trn video Leavy.avi

Phng php trn khc phc c nhng khuyt im ca phng phpFrame Differencing. Cc im bn trong lng i tng chuyn ng hay nhng

i tng dng chuyn ng trong khong thi gian di khng bxem nh l

background. Tuy nhin phng php ny chy khng tt i vi nhng video c

background chuyn ng.

Hnh 9: Kt qu chy thut ton khng tt i vi nhng video c background thay i

1.2.3 Running MedianTng tnh phng php Running Average. Bc u tin ca phng

php Median l xy dng ra m hnh background qua qu trnh hc trn n frame.


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Gi tr pixel ti v tr(x, y) ca m hnh background c xy dng bng cch chn

ra gi tr trung v ca n frame ti v tr(x, y).

Gi tr trung vc xc nh bng cch sau:

V d, vi mt dy frame tF1 ti Fn, ta xt pixel v tr(x, y) th cc gi tr

F1(x, y), F2(x, y),, Fn(x,y) s to thnh mt dy gi tr ca pixel (x, y)ng vi tng

frame nh

Bc u tin ta xp xp dy pixel ny theo th ttng dn

Gi tr trung v chnh l phn t nm chnh gia ca dy pixel sp xp th

t

Sau khi xy dng c m hnh background theo phng php trn th

nhng bc sau c thc hin tng tnh thut ton Running Average.

V c bn th phng php trn gn ging vi phng php Running

Average. Tuy nhin, phng php Running Median i hi chi ph lu tr v tnh

ton rt ln. Nu trong phng php Running Average, ta ch cn lu li tng gi tr

pixel ti mt vtr th trong phng php Running Median, ta phi lu li tt c cc

gi tr ca im nh ti mi frame.

u im:

chnh xc cao hn phng php frame differencing.

Khuyt im:

Tn chi ph trong khi hc ra m hnh background



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(fps)

VN-SIN-VE_02.avi 352 x 288 758 31

Leavy.avi 360 x 240 4362 30

Bng 4: Bng kt qu thc nghim khi chy thut ton Running Median

Hnh 10: Kt qu khi thc hin thut ton Running Average trn video VN-SIN-VE_02.avi

1.3 Cc phng php cao cpCc phng php c bn c cp trn ch chy tt i vi trng hp

background tnh khng chuyn ng. Tuy nhin trong thc t ta thng bt gp

nhng background c s thay i theo thi gian nh: cnh cy rung trong gi, s

giao ng ca mn ca, dao ng ca sng nc Do cc phng php c

cp di y s khc phc c vn trn

1.3.1 Running Gaussian AveragePhng php ny do Wren, Azarbayejani, Darrell, Pentland a ra vo nm

1997. Phng php ny t mt phn phi Gaussian ln s bin thin gi tr ca

mi pixel trong on video. V d, vi mt dy frame tF1 ti Fn, ta xt pixel v

tr(x, y) th cc gi trF1(x, y), F2(x, y), , Fn(x,y) s to thnh mt dy gi tr ca


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pixel (x, y)ng vi tng frame nh. Bng cch tnh trung bnh v phng sai ca

dy pixel ny ta xc nh c pixel background (gi trtrung bnh) v ngng (

lch nhn vi mt hng sno ).[5]

(1.5) (1.6)

Nhng pixel no c gi tr no tha | | th c xem lforeground. th c th chn bng k. [5]u im:

Xc nh c ngng cho tng im nh trn frame.

Khuyt im:

D bnh hng bi sng.



(fps)

VN-SIN-VE_02.avi 352 x 288 758 31

Leavy.avi 360 x 240 4362 30

Bng 5: Bng kt qu thc nghim khi chy thut ton Running Gaussian Average

1.3.2 Codebook tng chnh ca phng php ny l ti v tr mi pixel ca m hnh nn,

mt tp cc cluster vi tm v gii hn trong khng gian mu s c xy dng

nhm th hin s phn b ca pixel nntrong khng gian mu . Mi cluster nh

vy c gi l codeword, tp cluster ti mi vtr pixel c gi l codebook. [7]

Sau y l chi tit ca vic xy dng codebook:


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Xt ti mi v tr pixel, giX = {x1, x2, , xn} l dy gi tr hun luyn cho

pixel , C = {c1, c2, , cL} l codebook cho vtr pixel . Codebook ny gmL

codeword. Mi codeword civiI = 1, 2, , L gm 2 thnh phn:

Vector RGB B 6 thnh phn . Trong :

o : sng ln nht v nh nht ca pixel ic gn chocodeword.

o

: Tn s codeword th i xut hin

o : Maximum negative run-length xc nh khong thi gian lu nhttrong qu trnh hun luyn m codeword i khng xut hin, nu iln

hn mt ngng cho trc th chng t codeword i ch mang tnh tm

thi v cn phi loi ra khi codebook.

o : Thi gian u tin v cui cng codeword i xut hin.

Hnh 11: M hnh codeword [8]


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Trong khi hun luyn m hnh, mi gi trxt(c ly mu ti thi im t)

c so snh vi codebook hin ti tm xem codeword no khp vixt. xc

nh xem codeword no khp tt nht chng ta dng o m sc v sng. Chi

tit gii thut nh sau[8]:

I. L 0, C ( php gn)

II. For t=1 to N do

i. , ii. tm codeword trong | khp vi da trn hai

iu kin (a) v (b) sau

a. b.

iii. Nu C = hoc khng tm c khp trong (ii) th .Khi to mt codeword cL vi v

iv. Ngc li, cp nht codeword khp cm vi

}v

bng cch t v end for.

III. Vi mi codeword

, i = 1, ..., Lt

.

Xy dng codebook | } Vil ngng chotrc

Hai iu kin (a) v (b) trn c tha khi mu xt v cm gn v sng

caxtnm trong nhng vng bao sng ca cm


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Tip theo ta s xem nu mu sc v sng thay i s c nh hng g n

phng php trn:

Khi chng ta c mt pixel xt = (R, G, B) v mt codeword ci vi Ta c:

Color distortion c thc tnh bng cch sau:

Thut ton phn on foreground-background c cho trong bng sau:

Vi mi codeword ta tm khp vix da trn 2 iu kin:


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( ) Cp nhp codeword

nh trongII-iv ca thut ton xy dng codebook

{

Sau khi c c foreground, ta s dng b lc median v php

morphology loi b cc vng pixel nh ring l(thng l nhng pixel nhiu).

Cui cng l gom nhm cc pixel k cn c c foreground blob [7].

u im:

Do vic s dng hmu YcbCr nn phng php ny x l cc video trong

mi trng sng yu kh tt

Khuyt im:

Ci t phc tp.

Tc kh chm.



(fps)

VN-SIN-VE_02.avi 352 x 288 758 25

Leavy.avi 360 x 240 4362 23

Bng 6: Bng kt qu thc nghim khi chy thut ton Codebook


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Hnh 12: Kt qu khi thc hin thut ton Codebook trn video VN-SIN-VE_02.avi


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Chng 2. Cc phng php pht hin mu2.1 Pht hin mu bng phng php c bn

Phng php n gin nht ly c mu trong video l xt tng pixel

mu. Tng pixel mu trong nh sc so snh vi mt khong mu bng cch xt

tng knh mu ca pixel vi ngng gi tr ng vi khong mu ang xt,

nhng pixel no nm trong ngng cho php ny th c gi li, ngc li s b

loi b

Hnh 13. Kt qu lc mu tc da trn h mu RGB ca video 3_13.45.00.avi

Phng php ny cho kt qu khng tt trong iu kin c nhiu nhiu trong

nh. Trong hnh trn ta mong mun pht hin mu en ca tc ngi. Tuy nhin

mt s khu vc bng ti b pht hin nhm.

2.2 Pht hin mu bng m hnh Gauss2.2.1 Single Gaussian

2.2.1.1 Gii thiui vi phng phpc cp trn th chnh xc thng khng cao.

Do cho vic pht hin mu c chnh xc hn th ta p dng m hnh Gauss.

M hnh Gauss gip ta c thxc nh c mu ca mt pixel c phi cng mu

vi mu chng ta mun tm hay khng.


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tng chnh ca Gauss l ti mt pixel bt k c mu c. Ta tnh mc

ging ca mu c vi mu color ta cn tm bng cng thc sau [9]

| || (2.1)Hoc [9]:

(2.2)Trong :

: Means

: Covariance

n: tng s pixel

cj: gi tr mu ti pixel thj

Vi sl khong cch Mahalanobis

Sau ta so snh vi mt ngng cho trc xc nh xem mu c c phi

l mu color ta mong i hay khng. Vic chn ngng c nh hng rt ln n

kt qu ca thut ton ny.

c th s dng c m hnh Gauss ta phi tri qua hai bc l hun

luyn v phn lp

Thm mt vn na c t ra l chn knh mu no l ph hp. gim

bt snh hng ca nh sng th ta tnh ton trn knh mu HSV.

2.2.1.2 Hun luynM hnh Gauss c hun luyn trn mt b d liu mu c th. Xt yu cu

trong vic pht hin mu tc ca ngi, ta s hun luyn m hnh Gauss da trn

mt tp d liu mu tc ca ngi. Tp d liu ny c to ra bng cch trch ra t


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mu tc. y l hnh nh minh ha mu tc ca ngi dng cho vic hun luyn

m hnh Gauss:

Hnh 14: B dliu mu tc ca ngi

Da trn tp d liu ny ta s tnh c hai tham s Means v Covariance

ca m hnh Gauss theo cng thc sau [9]:

(2.3) (2.4)

chnh xc ca m hnh cng cao khi b d liu hun luyn cng ln.

2.2.1.3 Phn lpSau khi c c m hnh gauss, xc nh xem mu c ca mt pixel bt

k c phi l mu ta ang mun tm hay khng ta cn tnh xc sut mu c ca pixel

da vo hm Gauss. Xc xut ny c tnh theo cng thc (1) hoc (2) vi means

v covariance c bc hun luyn. V cng vi mt ngng chn trc

s gip ta phn lp xem mu c c phi l mu ta ang mun tm hay khng


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Hnh 15: Kt qu cho vic phn lp mu sdng m hnh Gauss trn video 4.avi. Mu ta

mong mun pht hin y l mu tc ca ngi

Hnh 16: Kt qu cho vic phn lp mu tc trn video 3_14.10.00.avi. Mu ta mong munpht hin y l mu tc ca ngi

Trong v d trn th nhng mu gn ging mu en ca tc nh: khu vc ti

ca bn gh, mu balo, mu ca b pht hin nhm. Tuy nhin khi so vi phng

php bn trn th cho kt qu tt hn. Nhng khu vc bng ti khng cn b pht

hin nhm.

2.2.2 Mixture of Gaussians tng chnh ca phng php Mixture of Gaussians cng tng t nh

phng php Single Gaussian. Nu nh phng php Single Gaussian ch da vo

mt hm Gauss xc nh xem mt mu c bt k c phi l mu chng ta mun

truy vn hay khng th phng php Mixture of Gaussians da vo nhiu hm


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Gauss xc nh iu . Mi hm Gauss trong Mixture of Gaussians c means v

covariance ring bit vi nhau.

Ti bc phn lp ca Mixture of Gaussians. Xc xut mu c ca mt pixelbt kc tnh theo cng thc sau [9]:

| | (2.5)Trong :

k: tng s Single Gaussian c trong Mixture of Gaussians

: tham s ca Single Gaussian thi. Vi |: Single Gaussian th iMi Single Gaussian c mean v covariance ring ca n.

Vic hun luyn m hnh Mixture of Gaussians c thc hin vi k thut

gi l thut gii Expectation Maximization(EM). Thut ton ny ginh slng

cc thnh phn Single Gaussian bn trong Mixture of Gaussians l bit trc

Vic phn lp vi m hnh Mixture of Gaussians c thc hin bng cch

so snh gi tr| vi mt vi ngng c chn trc2.3 Kt lun

Vi hng tip cn ca mt sphng php c cp trn y th ta c

cc u khuyt im nh sau:

u im:

Pht hin chnh xc mu ta mong mun tm kim

Tc x l nhanh.

Khuyt im:i vi nhng hng tip cn da vo mu sc th thng gp phi

mt skh khn nht nh nh sau:


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chnh xc ph thuc rt nhiu vo iu kin nh sng. Do vic la

chn hmu ng vai tr ht sc quan trng. lm gim snh hng ca

sng th ta x l trn nhng hmu nh: HSV, YCbCr

chnh xc ca phng php pht hin mu bng m hnh Gauss ph

thuc vo b d liu mu trong vic hun luyn. chnh xc cng cao khi

b d liu hun luyn cng ln.


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Chng 3. Pht hin theo dng mu snake3.1 Gii thiu

Theo [10], snake c nh ngha l hnh chiu ca mt ng vin lin tc

nm trn b mt nh. ng vin ny c thc xp vo mt trong cc loi sau:

(a) ng bin ca b mt gia hai min c tnh cht tng phn nhau, (b) ng

bao ngoi bng ca mt b mt nm trn nn khc, (c) ng thng nm trn b

mt m tng phn vi tnh cht cc b ca hai bn ng thng . tng

phn ca tnh cht c cp trn c nh ngha bi s khc nhau vcng

, hoc mu sc, kt cu hoc cc i lng trc quan phc tp khc; tuy nhin,

trong phm vi lun vn ny, chng ti ginh rng tng phn c th hindi dng khc nhau v mu sc ph hp vi cc thut ton lc mu.

tng chnh khi s dng snake l ta s dng cc dng mu c nh

ngha sn tm ra trong nh nhng i tng c vin ngoi khp vi cc dng

mu .

Hnh 17. Minh ha so khp snake trn nh

3.2 Biu dinTheo [10], chng ti nh nghasnake l ng cong B-spline ng nht, bc

ba v khng tun hon. ng cong B-spline ng nht, bc ba gmNphn on

ng cong

, gia cc ng cong

ny l lin tc bc hai. Cc phn on ny


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c tham s ha vi nh l trn ca bn im kim sot (control point) , c th hin nh sau: (3.1)Vi iu kin lin tc bc hai gia cc ng cong, ng thi tng trng s , chng ti s dng bn hm trn bc ba ph hp vi mi ng

cong sau y: , , , . Khi , phng trnhng cong c biu din di dngma trn nh sau:

(3.2)

Cho l vector cha ton b im kim sot cang cong, trong M=N+3. Khi xt trn ton bng cong, tham suc

chuyn i tng ng vi tham s

da trn vector G cha cc hm trn

ton cc . Gic nh ngha nh sau: {

(3.3)

Vector G(s)c dng chuyn i tham ss sang tham sutng ng v

chn ra hmBis dng trong cng thc xc nh ng cong .

(

)

(

) ( )

(3.4)


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Nh vy, ng cong c thc suy ra t tp cc im nm trn ng

cong, cc im ny c gi l im hnh mu (template point) cho trc nh sau:

( )

(

)

()

(3.6)

Hnh 18. Minh ha vic xy dng snake ttp template point c sn: nhng im mu trngl template point, nhng im mu xanh l control point

3.3 Khng gian trng thiTheo [11], khng gian trng thi l s nh x tuyn tnh vector khng gian

trng thi vo vector spline

(3.7)Q: spline-vector

X: shape-space vector

Q0: ng cong mu

W: ma trnNQNXhnh dng.


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(a) (b)

(c) (d)

(e) (f)Hnh 19. Su mc bin i affine c bn. (a) dch chiu ngang, (b) dch chiu dc, (c) quay,

(d) t l theo chiu dc, (e) t l theo chiu ngang, (f) t ltheo ng cho

m t xp x cc bin i c th c ca vt th trn mt phng hai chiu,

chng ta ch cn su php bin i affine trn hnh mu. Nhm cc php bin iaffine trn mt phng c xem nh mt lp cc bin i tuyn tnh vi hnh mu

Q0 v ma trn hnh dng (shape-matrix):

(3.8)


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Hai ct u tin trong Wbiu din cho s chuyn v ngang v dc. Bn mc

affine cn li c th hin thng qua kt hp tuyn tnh n gin trong bn ct cn

li. Nh vy trong cng thc nh x,Xng vai tr l trng s cho cc ct trong W.

(3.9)Di y l mt s v d cho cc php bin i:

: nh x vo hnh mu Q0. : dch hnh mu Q0 sang phi mt n v. : phng to hai ln hnh mu Q0ban u : xoay hnh mu i gc

ngc chiu kim ng h

(a) (b)


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(c) (d)Hnh 20. Minh ha p dng php bin i affine trn ng cong. (a) dch tri, (b) dch ln,

(c) thu nh, (d) quay

3.4 So khpVi mi phn on to nn ng cong Q, ng php tuyn c xem xt

c ct vi bin ca i tng trn hnh hay khng, chiu di ca ng php tuyn

ny cng nh th ng cong cng m st vi i tng ( di ca php tuyn

thng c s dng t 10-20 pixel). Gi

l hp (fitness) gia m hnh dng

mu v ng bin trong nh, c tnh nh sau: | | (3.10)

: v tr ca phn on ng cong thi xt trn nh,z(i): l v tr ca bin tm c gn nht ct php tuyn ti vtr ng cong

thi, : gi tr penalty nu khng tm thy ng bin no dc theo php tuyn tivtr ng cong thi.

Vi (3.1), khp c tnh theo cng thc sau:


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| (3.11)

: gi trc chn trc.

Hnh 21. Minh ha so khp khng gian snake vi bin tm c trn nh: ng mu ltemplate, ng mu vng l ng cong c kht cao nht


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Chng 4. Histogram of gradient (HOG)4.1 Gii thiu

Histogram of gradient (HOG) l c trng c dng nhiu trong lnh vc

pht hin i tng. K thut ny c xut bi Bill Triggs v Navel Dalal vo

nm 2005 ti vin nghin cu INRIA.

tng chnh trong t trng HoG l hnh dng v trng thi ca vt c th

c c trng bng s phn b v gradient v hng ca cnh. c trng ny

c pht trin da trn SIFT, c trng HOG c tnh trn c mt vng. Do s

bin thin mu sc trong cc vng l khc nhau, kt qu l mi vng s cho ta mt

vector c trng ca n. V vy c c c trng ca ton b ca s (window)

ta phi kt hp nhiu vng lin tip li vi nhau.

c trng HOG c mt s bin th thng gp nh: R-HOG, R2-HOG, C-

HOG [10]. Cc c trng ny khc nhau cch phn b v hnh dng ca cc nh

trong hnh

Hnh 22: R-HOG v C-HOG [10]

R-HOG: mi c hnh dng l hnh ch nht

C-HOG: mi c dng hnh trn


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4.2 Rt trch c trng HOG trong nhT mt nh cho trc, qua vic rt trch c trng trn nh cho ta c

mt vector i din cho i tng . Vic rt ra c cc vector c trng nh vy

gip chng ta chuyn khng gian nh sang khng gian vector c th x l v tnh

ton n gin hn trn my tnh.

Phng php tng quan tnh ton c trng HOG trn mt ca s

(window) bt k:

Phn chia ca s (window) cn tnh HOG thnh nhiu khi (block). Mi

khi gm nhiu (cell). Cc phi c kch thc bng nhau, slng trong mi khi l bng nhau

Cc khi ny c t chng ln nhau, khong cch gia hai khi con lin

tip nhau phi l mt hng s

Hnh 23: Mi khi (block) gm nhiu (cell). V d trong hnh trn ta thy mt khi gm 9

[10]

Hnh 24: Cc khi c xp chng ln nhau [10]

Sau khi phn chia ca s cn tnh ton HOG nh trn. Ta tin hnh rt

trch c trng nh sau:


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o Tnh ton c trng trn tng vng:

Tnh ton c trng trn tng nh (cell)

Tnh ton c trng trn tng khi (block) bng cch kt hp

cc li vi nhau

o Thu thp c trng ca cc vng trn nh.

Tnh v chun ha vector c trng cho tng block

Thu thp cc c trng HOG cho cc ca s

Sau y l chi tit thut ton rt trch c trng HOG. minh ha thut

ton, ta c th chn mt v dnh sau tnh vector c trng HoG v s lng

thnh phn ca vector : gi s ta c mt nh vi kch thc l 64x128 pixels. Tachia nh ny thnh cc cell vi kch thc 8x8 pixels. V ta chn kch thc ca

block l 2x2 cell.

Bc 1: chuyn nh trong khng gian RGB sang nh dng GRAY SCALE,

sau tin hnh cn bng histogram trn nh GRAY SCALE gim i nh

hng ca sthay i nh sng.

Bc 2: tnh s bin thin mu sc ti tt c cc pixel ca nh GRAY

SCALE theo chiu X [-1 0 1] v theo chiu Y [ ]v thu c 2 nhgradient-x v gradient-y c kch thc bng kch thc nh GRAY SCALE

v cho thy c s bin thin mu sc ni trn.

Bc 3: tin hnh tnh gc v hng bin thin mu sc t 2 nh gradient-x

v gradient-y nh hnh vbn di :


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Bc 7: tnh ton vector c trng cho ton b nh (64x128). Ta ch cn

ghp cc vector c trng ca tng block li vi nhau c vector c

trng ca cwindow. Lu rng cc block khng t tch bit nhau m gi

ln nhau, cch nhau mt khong bng kch thc ca cell theo mi chiu

(hoc bng phn na kch thc ca cell). Nh vy vector c trng ca

window = s block trong window * 36 = ((64 16)/8 + 1)*((12816)/8 +

1)*36 = 7*15*36 = 3780 thnh phn.

Bc 8: Sau cng ta chun ha li vector c trng ca c window.

Hnh 26: Cc bc rt trch c trng HOG [4]


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4.3 Chun ha vector c trng cho tng block chun ha vector c trng cho c window, ta chun ha c trng cho

tng vector ca tng block. Ta s dng cc phng php sau chun ha vector

c trng ca tng block. [4]

L2-norm:

(4.1) L1-norm:

(4.2) L1-sqrt:

(4.3)Trong :

: vector c trng ban u ca mt block (cha chun ha). : k-norm ca vi k= 1, 2 : Hng s nh

Theo Dalal v Triggs, L2-norm v L1-sprt cho cng kt qu. Trong khi

L1-norm cho kt qukm hn. Tuy vy vic chun ha bng mt trong ccphng

php trn vn cho kt qu tt hn i vi nhng vector khng chun ha.

4.4 Integral ImageTbc 5, 6, 7 trong s trn ta nhn thy l ta cn tnh c trng cho

tng cell rt nhiu ln, do vy y c th s dng k thut integral image lm

tng tc tnh ton. [11]


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Hnh 27 Integral Image [11]

nh Integral l nh c tnh cht sau: gi tr ca pixel ti v tr (x, y) bt k

trong nh Integral bng vi tng s cc pixel thuc hnh ch nht c to bi imgc tri trn cng ca nh (0,0) v im (x, y)ang xt.

(4.4)Hnh 28 Cng thc tnh integral image [12]

Trong :

i: nh ban u ii: nh integral ca i

nh integral ca mt nh cho trc c tnh theo cng thc trn. Nh p

dng integral image vo tnh gi tr ti mi bin ca vector c trng ca tng cell

rt nhanh chng. Gi s ta cn tnh gi tr vector c trng ca cell D (nh trn

hnh) ti thnh phn i, khi ta ch cn ly nh integral image ca nh bin i tnh

trn, ta thu c nh ii. Khi gi tr cn tnh s bng: (4.5)Lu rng ta ch cn tnh integral image duy nht 1 ln v dng n tnh

cho ton bcc cell. u im ca cch tnh ny l ta ch cn bit gi trnh Integral

ca bn gc hnh ch nht l c th ddng tnh c vector c trng ca hnh

ch nht


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4.5 Cng thc quy tnh Integral Image tnh nhanh Integral Image ta c th dng cng thc quy sau [11][12]:

(4.6) (4.7)Vi gi tr khi to:

Trong :

i: nh ban u

ii: nh integral ca i

s: gi tr trung gian

Ta c th hiu cng thc quy trn nh sau, tnh ii ta s tnh theo tng

dng v tnh dng tip theo ta s dng kt qu ca dng trc. Nh vy c ththy phc tp ca vic tnh integral image l O(n)


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Chng 5. Support Vector Machines (SVM)5.1 Gii thiu

Trong mc ny chng ta s bn v Support Vector Machine (SVM). SVM l

mt phng php trong vic phn loi d liu tuyn tnh v khng tuyn tnh. C

nhiu phng php phn loi d liu nh: phn lp da vo cy quyt nh, phn

lp da vo lut, phn lp Bayesian, phn lp theo lan truyn ngc Tuy nhin

trong lnh vc ca ti th SVM l phng php phn lp c chn s dng.

Bi bo u tin vSupport Vector Machine c gii thiu vo nm 1992

bi Vladimir Vapnik v hai ng s Bernhard Boser v Isabelle Guyon, mc d nn

mng c bn ca SVM c tnm 1960 (bao gm cc cng vic c thc hin

rt sm bi Vapnik v Alexei Chervonenkis trong l thuyt hc thng k). [13]

Trc khi i vo tm hiu phng php SVM, ta phi bit c cc khi

nim v siu phng phn chia tuyn tnh, support vector

5.2 Cc khi nim c bn5.2.1

Siu phng phn cch

Cho trc tp d liuD gm (X1, y1), (X2, y2), ..., (X|D|, y|D|).

Trong Xi l mt tp cc b hun luyn tng ng vi nhn lpyi. Miyi

s nhn mt trong hai gi tr hoc l +1 hoc l -1 (yi {+1, -1}).

Phng php phn lp SVM s tm ra ng phn lp tt nht phn

chia tp d liu ny thnh tng lp tch bit ra vi nhau. Theo [14], phng trnh

tng qut ca mt ng phn chia nh vy c biu din di dng sau:

(5.1)Trong :

W: Vector trng s, W = {w1, w2,,wn).

n: S thuc tnh (hay cn gi l s chiu ca d liu).


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b: Mt i lng v hng, thng c xem nh l mt nghing (bias).

i vi trng hp d liu hai chiu (hai thuc tnh) th phng trnh trn

biu din ca ng thng phn chia. Nu d liu ca chng ta l ba chiu thng phn chia gia hai tp s l mt mt phng phn cch. Tng qut cho d liu

n chiu th sc phn cch bi mt siu phng. Chng ta s s dng thut ng

siu phng (hyperplane) chn ranh gii quyt nh m chng ta mun tm

kim bt chp slng thuc tnh.

Hnh 29: ng mu vng trn hnh l ng phn chia i vi tp dliu gm hai thuctnh [15]


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Tuy nhin trong thc t ta c thtm c v s nhng siu phng phn chia

trn cng mt tp d liu. V chng ta mun tm ng thng phn chia sao cho tt

nht, c ngha l c sai st phn loi b nht trn b d liu.

Hnh 30: Mt b dliu hai chiu c phn chia tuyn tnh. C v hn ng thng phnchia tuyn tnh c th . Vy ci no l tt nht? [14]

Do mc tiu ca phng php phn lp SVM l tm mt siu phng phn

cch gia hai lp sao cho khong cch l (margin) gia hai lp t cc i.

Siu phng c bin ln nht (maximum marginal hyperplane) s c

chn nh l siu phng phn chia tp d liu mt cch tt nht. Trong hnh bn

di, ta thy c hai siu phng c thphn chia c v nhng bin ca n.Trc khi i vo nh ngha ca bin (margin), hy nhn vo hnh trn mt cch

trc quan. C hai siu phng u phn tch tt c nhng b d liu cho trc. Mt

cch trc quan, siu phng vi bin ln hn schnh xc hn trong vic phn

loi cc b d liu trong tng lai so vi siu phng c bin nhhn. iu ny

l l do ti sao (trong sut giai on hc hay hun luyn), SVM tm nhng siu

phng c bin ln nht, gi l MMH (maximum marginal hyperlane). Siu


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phng c bin ln nht l siu phng c khong cch t n ti hai mt bn ca

n th bng nhau (mt bn song song vi siu phng). Khong cch tht ra l

khong cch ngn nht t MMH ti b d liu hun luyn gn nht ca mi lp.

Siu phng c bin ln nht ny cho chng ta mt s phn loi tt nht gia cc

lp

Hnh 31: Trong hnh ny chng ta c 2 siu phng phn chia tuyn tnh cng vi bin can. Ci no tt hn? Ci c bin ln hn th sc chnh xc cao hn[14]

Siu phng phn cch c vai tr quan trng trong vic phn lp, n quyt

nh xem mt b d liu s thuc v lp no. thc hin vic phn lp, SVM ch

cn xc nh xem mt b d liu nm v pha no ca siu phng phn cch

(5.2) D(x) < 0: b d liu s nm pha di siu phng phn cch

D(x) = 0: b d liu s nm trn siu phng phn cch

D(x) > 0: b d liu s nm pha trn siu phng phn cch


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5.2.2 Support vectorsTa c phng trnh tng qut ca siu phng

Ta xt trn v d sau:Vi b d liu hun luyn c hai thuc tnhA1 vA2: X={x1, x2}, vix1, x2 l

gi tr ca thuc tnhA1, A2. W = {w1, w2}. Phng trnh siu phng c th vit li:

Trong :

w0tng ng vi hng sbtrong phng trnh tng qut ca siu phng

V vy mi im nm trn siu phng phn cch tha mn:

Tng t, nhng im nm di siu phng phn cch phi tha mn: Bng cch iu chnh trng sw0 ta c:


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Hnh 32: ng biu din H1 v H2, ng mu l khong cch Euclidean ca hai im 1v 2. ng mu xanh l khong cch Euclidean nh nht [15]

iu ny c ngha l nu bt k b no nm ti hoc trnH1 u thuc v lp

+1, v bt k b no nm ti hoc diH2u thuc v lp -1.Kt hp 2 bt ng

thc trn ta c:

Mi b hun luyn nm ti cc mt bin H1 hayH2 tha mn phng trnhtrn c gi l support vectors. Support vectors l nhng b gn vi siu phng

phn chia tuyn tnh (MMH) nht.


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Trong hnh bn di, support vectors l hnh trn c vin dy hn. Ta thy

rng cc support vectors l nhng b kh phn lp nht v cung cp nhiu thng tin

nht cho vic phn lp.

Hnh 33: Cc support vector trong SVM. Cc support vector l nhng hnh c vin dy hn.

[14]

5.2.3 Bin (Margin)Tcc iu trn chng ta c tha ra mt cng thc cho vic tnh bin

ln nht. Khong cch t siu phng phn chia n mi im ti H1 l

Trong

l khong cch Euclidean chun ca Wl

.Vi W = {w1, w2}

khi . [14]Theo nh ngha, khong cch t siu phng nH1 bng vi khong cch t

mi im tiH2n siu phng. V vy, kch thc ca bin cc i l.

5.3 Phn lp d liu


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5.3.1 Trng hp d liu c th phn chia tuyn tnh cVic hun luyn SVM vi mc ch l tm ra cc support vectors v

MMH. MMH l ranh gii phn chia tuyn tnh gia cc lpv v thSVM tng

ng c thc s dng phn lp d liu m d liu c th phn chia tuyn

tnh. Chng ta xem SVM c hun luyn l SVM tuyn tnh.

Sau khi hun luyn SVM, chng ta s phn loi cc b mi. Da trn cng

thc Lagrangian ta c [14]:

Trong :

yi l nhn lp ca support vectorXi

XTl mt b test

(nhn t Lagrangian)

b0 l bin sc xc nh bi s ti u ha hay cc thut ton SVM

l l slng cc support vectors.

MMH c th c xem nh ranh gii quyt nh trong vic quyt nh

xem mt b test bt ks thuc vo lp no. Cho mt b testXT, chng ta gn n

vo phng trnh trn, v sau kim tra du ca kt qu. T ta s bit c b

test sri vo mt no ca siu phng. Nu du l dng, thXTri vo pha trn

ca MMH, v SVM on rng XT thuc v lp +1. Nu du l m, th XT nm ti

hoc di MMH v nhn lp c on l -1.

5.3.2 Trng hp d liu khng th phn chia tuyn tnh cTrong phn trn chng ta cp n trng hp SVM phn lp nhng d

liu c th phn chia tuyn tnh, nhng nu d liu khng th phn chia tuyn tnh


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th sao? Trong trng hp ny khng c ng thng no c th vc phn

chia cc lp ny. SVM tuyn tnh m chng ta hc th khng em li li gii kh

thi trong trng hp ny.

Hnh 34: Mt trng hp n gin trn khng gian 2 chiu. Khc vi dliu c th phnchia tuyn tnh c trn hnh 6.20, y ta khng th v mt ng thng phn chia 2 lp.

Do vy ranh gii quyt nh y th khng tuyn tnh. [14]

Tuy nhin hng tip cn ca SVM tuyn tnh c thc mrng to raSVM khng tuyn tnh cho vic phn lp cc d liu khng th phn chia tuyn

tnh (hay gi tt l d liu khng tuyn tnh). Nhng SVM nh vy c khnng tm

nhng ranh gii quyt nh khng tuyn tnh (nhng mt khng tuyn tnh) trong

khng gian u vo.

Lm th no m rng tip cn tuyn tnh?. Chng ta thu c SVM phi

tuyn bng cch mrng SVM tuyn tnh nh sau. C hai bc chnh:

Bc 1: Ta chuyn d liu ngun ln mt khng gian nhiu chiu hn bng

cch s dng nh x phi tuyn. Mt vi nh x phi tuyn thng thng c th

c s dng thc hin bc ny

Bc 2: Tm nhng siu phng trong khng gian mi ny. Cui cng chng

ta li quay li vn ti u bnh phng c gii quyt s dng cng


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thc SVM tuyn tnh. Siu phng c bin ln nht c tm thy trong

khng gian mi tng ng vi siu b mt phn chia khng tuyn tnh trong

khng gian ban u.

Tuy nhin vn ny nm ngoi phm vi ca ti. Chi tit v vn ny

c cp [13]

5.4 Nhn xtGing nh cc phng php my hc khc, phng php SVM hot ng

tt n phi c hun luyn trn mt b d liu ln, bao qut y cc trng

hp.

u im ca phng php ny l c chnh xc cao. Tuy nhin n gp

phi khuyt im l tn thi gian v chi ph cho vic hun luyn.


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Phn B: ng dng bi ton pht hin i tng trong lp hc

Chng 6. Gii thiu bi ton6.1 Gii thiu v h thng pht hin hc sinh trong lp hc

Vi mc tiu t ra ca ti l tm hiu cc k thut pht hin ngi

trong video gim st lp hc th chng ti ra mt phng php gii quyt

chnh nh sau:

Hnh 35: M hnh chung ca h thng pht hin hc sinh trong lp

H thng ca chng ti gm c ba bc chnh:

Bc 1: Hair detection - bng cch s dng c trng vmu tc, phng

php ny s pht hin ra tt c nhng v tr c mu ging vi mu tc. V tr

ny c th l v tr u ngi hoc nhng ch ti, khung ca s c mu

en Mc tiu ca bc trn l c lng ra nhng v tr no m u

ngi c th xut hin.


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Hnh 36: Kt qut c sau khi thc hin vic pht hin mu tc

Trong hnh trn ta thy kt qu vic pht hin ra mu tc tng i tt. Pht

hin c hu ht cc mu tc. Tuy nhin cng gp mt s nhiu nh: bng ti ca

bn gh, mu o gn ging mu tc kh nhng nhiu chng ti p dng

bc tip theo.

Bc 2: Sau khi c c kt qu ca vic pht hin mu tc th ta s dng

bc hai kim tra li kt qu ca bc u tin. Mc tiu ca bc ny

nhm loi b cc nhiu trong bc u tin. Ti bc hai ta p dng hai

phng php:o Phng php 1: s dng b phn lp SVM v c trng HOG ca nh

phn pha trn u ngi


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Hnh 37: Kt qu sau khi pht hin u ngi bng SVM v HOG

o Phng php 2: pht hin u ngi bng phng php omega shape

Bc 3: bc ny nhm mc tiu so snh hai phng php bc hai.

6.2 Mt s gi thit t ra trong ti Do camera khng th thu hnh ht trn vn ton b lp hc. Do h thng

s ch pht hin hc sinh trong mt dy bn c quan st r rng (bn phi

hoc bn tri). Nhng vng nm ngoi phm vi s b qua trong qu trnh x

l


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Hnh 38: Mt hnh minh ha trong video 2.avi

Hnh trn ta thy rng video chthu hnh c dy bn bn phi. Do h

thng ch pht hin nhng ngi dy bn ny. i vi nhng ngi thuc dy bn

bn tri sc bqua. thc hin c iu ny ta da vo mt hnh mask nh

hnh bn di. Vng trng trong hnh l vng s khng x l

Hnh 39: Khu vc gii hn xl ca h thng. Vng trng l vng khng xl

Mu tc ca cc i tng bn trong video phi l mu en


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i vi cc i tng khng thnhn r c s b qua

Hnh 40: Mt i tng khng nhn r trong video 3.avi

6.3 M t d liu video lp hc6.3.1 Mc ch s dngTp d liu c s dng trong ti ca nhm l video v lp hc. B d

liu trn c s dng cho hai mc ch chnh:

Mt svideo c ly lm d liu hun luyn cho m hnh SVM

Cc video cn li c s dng test kt qu cho vic detect u ngi

trong lp hc

6.3.2 Chi tit v b d liuB d liu ca nhm hin gigm 11 video. Sau y l phn m t chi tit

v mi video


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6.3.2.1 Thng s videoTn video Thi gian

Tng s

frame

Kch thc

mt frame

Dung lng

(MB)

Frame Rate

(fps)

1.avi 00:14:59 26998 640x480 66.5 30

2.avi 00:10:59 19772 640x480 368 30

3.avi 00:11:00 19803 640x480 351 30

4.avi 00:11:00 19800 640x480 359 30

5.avi 00:10:57 19730 640x480 299 30

6.avi 00:04:58 8956 640x480 22.5 30

7.avi 00:10:59 19775 640x480 336 30

8.avi 00:10:59 19771 640x480 373 30

9.avi 00:10:59 19778 640x480 299 30

10.avi 00:10:58 19776 640x480 326 30

11.avi 00:10:59 19776 640x480 268 30

Bng 7: Bng thng k tp video dliu

6.3.2.2 c trng d liuc im chung ca cc video trn l cht tng hnh nh khng c tt

lm

Do phng php ca nhm ph thuc rt nhiu vo bc pht hin ra mu

tc ca ngi. Do nhm gp phi mt skh khn gy nhiu lm nh hng n

kt qu pht hin mu tc nh sau:


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Hnh 41: Mt skh khn trong video 2.avi

Khung mu trong hnh 1 cho ta mt v d vtrng hp hai sinh vin c

u chm vo nhau. Do nu da vo mu tc th khng th pht hin

c l c hai u khc nhau

Khung mu xanh trong hnh 1 minh ha cho vic mu tc ca mt sinh vin

ging vi mu ca. Do khng th pht hin c vtr u ca sinh vin

trong trng hp ny


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Hnh 43: Kh khn trong video 1.avi

Kh khn thng xuyn gp phi trong cc video l nhng bng ti ca bn

do vy lm cho vic pht hin mu tc skhng c chnh xc


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Chng 7. Pht hin mu tc trong video lp hc7.1 Gii thiu bi ton

Bi ton pht hin mu tc ca ngi trong video l bi ton ng vai tr

quan trng trong h thng gim st lp hc. y l bc tin xl u tin ca h

thng pht hin phn u ca ngi. Bng cch da vo thng tin mu tc t

c th tm ra nhng v tr tim nng m ti c th l phn u ca hc sinh.

Phng php c p dng ti bc ny l s dng m hnh Gauss. chnh xc

ca h thng ph thuc rt nhiu vo bc pht hin mu tc ngi. Mu sc ta cn

quan tm y l mu tc ca hc sinh.

7.2 Phng php c th pht hin c chnh xc mu tc ca ngi th chng ti chn

hng tip cnl s dng phng php lc mu bng m hnh Single Gauss.

Bc u tin cn thc hin khi p dng phng php ny l giai on xy

dng m hnh mu cho Single Gauss. M hnh ny c xy dng bng vic hun

luyn trn mt tp d liu mu tc ca ngi. Tp d liu ny c to ra bng

cch trch ra t mu tc ca ngi trong video. Sau y l hnh nh minh ha mu

tc ca ngi dng cho vic hun luyn m hnh Gauss:

Hnh 44: B dliu mu tc ca ngi

Tp d liu hun luyn gm 20 nh. Mi nh c kch thc 100x100 pixels.

gim bt s nh hng ca nh sng th chng ti chn tnh ton trn

knh mu HSV.


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Hnh 45: Knh mu HSV [16]

Trong knh mu HSV, th i vi knh H snh hng ca mu en l rt

thp. V vy vic tnh ton Means v Covariance s da vo hai knh mu S v V

trong h mu HSV.

Da trn tp d liu ny ta s tnh c hai tham s Means v Covariance

ca m hnh Gauss theo cng thc sau:

(7.1)

(7.2)Trong :

cj l mt vector gm hai thnh phn S v V trong knh mu HSV ti pixel

th j.

Sau khi xy dng c m hnh mu cho Gauss. Bc tip theo ta s sdng m hnh ny trong vic phn lp. Vic phn lp c thc hin nh sau:

Tnh xc sut mu c ca pixel da vo hm Gauss. Xc xut ny c tnh

theo cng thc (1) hoc (2) trongchng 2 vi means v covariance c bc

hun luyn. V cng vi mt ngng chn trc s gip ta phn lp xem mu

c c phi l mu ta ang mun tm hay khng.


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Hnh 46: Kt qu cho vic phn lp mu tc sdng m hnh Gauss trn video 4.avi

T kt qu trn ta s tm cc hnh ch nht bao quanh khu vc mu tc nh

hnh bn di.

Hnh 47: Kt qu trong vic tm cc vng hnh chnht bao cc mu tc

7.3 Nhn xtKt quthu c trong vic pht hin mu tc l tng i tt. Tt c mu

tc u c pht hin. Tuy nhin trong mt vi trng hp sau th vic pht hin

mu tc s khng chnh xc:


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- i vi mt s mu gn ging vi tc ngi u b pht hin nhm nh:

mu en ca nhng bng ti, mu o en, mu en ca khung ca

- Ngoi ra h thng cng s khng pht hin c nhng hc sinh c tc

vng, tc nhum hoc thm ch l khng c tc, tc qu ngn


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Chng 8. p dng c trng HOG cng vi SVM trong vic phthin hc sinh trong lp hc

8.1 Gii thiu bi tonChng ny s m t cch ng dng b phn lp SVM cng vi c trng

HOG trong vic pht hin phn pha trn (upper body part) ca hc sinh trong vdeo

gim st lp hc. u vo trong bc ny l nhng khu vc c mu tc c pht

hin ra chng 7. Kt qu ca bc pht hin mu tc c trnh by trong hnh

bn di.

Hnh 48: Kt qu trong vic pht hin mu tc chng 7

Vi ginh ca bi ton l ch xl i vi dy bn bn tri. Trong hnh

trn ta nhn thy rng ngoi vic pht hin chnh xc mu tc ngi th cn c mt

s nhiu nh khu vc ti ca bn gh, mu o ca hc sinh gn ging mu tc

Vi nhn xt rng din tch ca phn u ngi l tng i ln. V vy

gim bt nhiu bc trn chng ti da vo din tch ca hnh ch nht bao

quanh vng u xc nh. y ta cho rng din tch phn u nh nht l 100


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v ln nht l 10000. i vi nhng vng c din tch qu nh hoc qu ln th ta

c th b qua. Sau khi loi bi nhng vng c din tch nm ngoi khong trn.

Kt quthu c nh sau:

Hnh 49: Kt quthu c khi loi i nhng vng c din tch qu nh hoc qu ln

Ta thy rng tuy loi bc phn ln nhng nhiu. Tuy nhin vn cn

c mt s nhiu khng loi trc. Do vy mc tiu ca phng php s dng b

phn lp SVM cng vi c trng HOG s nhm loi bi cc nhiu trn.

Cc bc chnh trong vic pht hin phn thn trn ca ngi bng phng

php SVM v HOG sc thc hin nh sau:

T tp hun luyn cha cc nh positive v negative ca phn thn trn

ngi. Ta rt trch ra c trng HOG v t hun luyn cho b phn

lp SVM

T cc v tr c mu ging mu tc, ta rt trch ra c trng HOG v sau

s dng b phn lp SVM xc nh xem ti c phi l phn thn

trn ca ngi hay khng


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Hnh 50: Qu trnh pht hin i tng bng SVM v c trng HOG[4]

8.2 Hun luyn cho b phn lp SVM SVM c th thc hin vic phn lp, bc u ta phi hun luyn SVM.

D liu cho vic hun luyn ny gm c hai b:

B d liu positive: y l b d liu cha cc hnh nh v phn thn trn

ca ngi. Mi nh phi bao gm phn u ca ngi v hai vai. B d liu

trn gm c tng cng 352 nh. Mi nh c kch thc 48x48 pixels. Sau

y l mt snh trong b d liu trn

Hnh 51: Mt s hnh nh trong b positive

B d liu negative: cha cc hnh nh bt k, nhng hnh nh ny khng

phi l phn trn ca ngi. B d liu trn c 425 nh vi kch thc bt k


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Hnh 52: Mt hnh bt k trong tp negative

i vi mi b d liu ta s tin hnh vic rt trch c trng HOGnh

cp chng 4. Tham sc s dng trong vic rt trch c trng HOG l:

Tng s bin: 9

Kch thc mt : 4x4 pixels

Kch thc ca mt khi: 2x2 cells

chng lp ca cc khi: 0.5

Tc thc thi trong vic rt trch c trng HOG l kh chm do tn nhiu

chi ph tnh ton.

Sau khi rt trch c trng cho b d liu positve v negative ta s tin

hnh hun luyn cho b phn lp SVM. i vi c trng HOG c rt trch ra tb positive ta s gn nhn lp l 1 v nhn lp l 2 cho b negative. y ta s

dng b phn lp SVM loi Linear.


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8.3 Tin hnh phn lpSau khi tri qua bc hun luyn cho b phn lp SVM, ta s s dng m

hnh tin hnh phn lp. Qu trnh thc hin phn lp c tin hnh nh

sau:

Ti mi hnh ch nht bao vng mu tc, ta s mrng hnh ch nht sau

cho n bao ton b khu vc phn thn trn ca ngi:

Hnh 53: Mrng hnh chnht bao vng mu tc

Trong hnh trn, hnh ch nht c mu trng l hnh bao vng mu tc. T

hnh ch nht ny ta tin hnh mrng ra sao cho n bao ton b vng thn pha

trn ca ngi. Kt quthu c hnh ch nht mu nh hnh bn trn.

Sau khi m rng vng mu tc, ta s tin hnh rt trch c trng HOG

trong vng ny. Vic rt trch c trng c thc hin nh sau:

iu chnh kch thc ca vng hnh ch nht v kch thc

48x48 pixels Rt trch c trng HOG trong vng 48x48 pixels

Sau ta a c trng HOG ny vo b phn lp SVM thc hin vic

phn loi. Nu SVM phn loi ra nhn lp l 1 ta s kt lun rng vtr l phn

trn ca u ngi. m bo s chnh xc trong qu trnh phn loi th ti mi


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mt v tr mu tc, ta s tin hnh vic pht hin phn thn trn ca ngi ti v tr

v nhng v tr xung quanh.

Hnh 54: Pht hin phn thn trn ca ngi ti v tr mu tc v nhng v tr xung quanh

Trong hnh trn, vng hnh ch nht mu l vng uc SVM phn loi l

phn thn trn ca ngi, hnh ch nht mu xanh th khng phi l phn thn trn

ca ngi. Bc k tip ta s tin hnh kt hp nhng hnh ch nht mu li vi

nhau thnh kt qu cui cng. Vic kt hp c thc hin nu chng c phn din

tch chung chim hn 70%


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Hnh 55: Kt hp cc hnh chnht li vi nhau. Hnh chnht mu xanh l kt qu cavic kt hp cc hnh chnht

Kt qu cui cng ta sthu c nh hnh bn di. Kt qu ca vic pht

hin phn thn trn ca ngi c hin th trong hnh ch nht mu . Cc nhiu

c loi b hon ton.

Hnh 56: Kt qu cui cng ca vic pht hin phn trn ca ngi

8.4 Kt qu thc nghimKt qu thc nghim: Cu hnh my Pentium Dual Core, 2.5 GHz, 2GB

RAM


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Tn video Kch thc frame Tng s frame Tc xl (fps)

1.avi 640x480 26998 0.55

2.avi 640x480 19772 1.85

3.avi 640x480 19803 0.53

4.avi 640x480 19800 0.67

5.avi 640x480 19730 0.90

6.avi 640x480 8956 0.55

7.avi 640x480 19775 1.32

8.avi 640x480 19771 0.55

9.avi 640x480 19778 8.5

10.avi 640x480 19776 0.62

11.avi 640x480 19776 0.85

Bng 8: Kt qu ca vic pht hin i tng bng phng php phn lp SVM i vi bdliu


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Hnh 57: Kt qu khi chy vi video 3.avi

8.5 Nhn xtPhng php pht hin phn trn ca ngi bng cch p dng b phn lp

SVM cng vi c trng HOG c chnh xc cao. Tuy nhin tc x l khng

t thi gian thc do chi ph trong vic tnh ton HOG l tng i ln. cho m

hnh SVM c chnh xc v hiu qu th ph thuc rt nhiu vo tp d liu hun

luyn. Tp d liu ny phi thu thp ln v khch quan. Mt iu quan trng na

l kt qu ca phng php ny ph thuc rt ln vo bc pht hin mu tc

ngi. Do trong cc trng hp m khu vc u ca hc sinh che lp vi nhng

vt dng c mu ging mu tc hoc l hai hay nhiu phn u ca hc sinh chm

li vi nhau (nh cc hnh bn di) th vic pht hin mu tc s khng cn chnh

xc


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Hnh 58: Trng hp pht hin mu tc khng chnh xc do mu en ca tc trng vi muen ca laptop v mu o

Hnh 59: Trng hp mu tc dnh vi mu o

Hnh 60: Trng hp hai u chm li vi nhau


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Chng 9. p dng Snake trong vic pht hin hc sinh trong lphc

9.1 Gii thiuPhng php ny c p dng kim tra li tnh chnh xc ca kt qu rt

trch c sau khi p dng b phn lp SVM. Bng cch so khp cc ca s kt qu

vi cc dng mu c sn, phng php ny s gip loi b cc nhiu m b phn

lp nhm

Hnh 61. Cc trng hp c th xy ra nhm ln sau khi p dng b phn lp SVM

u vo ca bc ny l kt qu cabc lc mu tc v kt qu sau khi p

dng b phn lp SVM a ra.

9.2 Xy dng d liuMc d c s tng ng v hnh dng bao ngoi ca phn trn c th

ngi, nhng i vi tng i tng ring l th s khc nhau l rt a dng: kch

thc, gc nghing, ... V vy, m bo cc dng mu c thbao qut cnhiu nht cc trng hp, d liu cng cn c mt lng ln cc dng mu cho

trc.

Trong cc nghin cu, dng mu thng dng nht l ng vin th hin

vng u-vai ca ngi, c gi l omega shape.


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Hnh 62. Mt s dng mu omega c dng so khp

Tuy nhin, do trong video gim st lp hc, s trng lp gia cc i tng

ngi kh nhiu, dn n s che lp phn ln phn trn c th, nht l phn vai, hn

na nhiu khi tin hnh pht hin ng bin l rt ln, chnh xc khi tnh

ton khp gia dng mu omega trn nh l khng cao.

Hnh 63. Kt qu sau khi sdng dng mu omega kim tra cc vng u vai ngi trongnh, do nhiu khin kt qu khng tt

Do , chng ti ngh s dng dng mu na vng cung, i din cho na

phn trn ca tc ngi. iu ny loi bc vn v s che lp ca ngi, tuy

nhin do dng mu n gin hn dng mu omega, nn khi s dng dng mu ny

vn xy ra trng hp so khp vi hnh dng m khng phi l na phn trn ca

tc ngi. D liu c xy dng sn nh sau:

Hnh 64. Cc dng mu na vng cung c dng so khp

ng vi mi dng mu cho trc, mt khng gian dng mu c khi to

da trn cc php bin i affine t dng mu gc (xem 3.3), v tin hnh so


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khp vi nh sau khi c lc mu tc ng vi v tr ca sc xc nhn l c

phn u ca ngi trong video. Hn na, tng tnh chnh xc khi s dng dng

mu, chng ti tn dng thng tin v khng gian trong lp hc, l nhng

ngi gn camera hn sc so khp vi dng mu ln hn, nhng ngi xa

v cui lp sc so khp vi dng mu nhhn.

Hnh 65. Kt qu khi so khp cc dng mu na vng cung vi cc phn u ca ngi c pht hin

9.3 Nhn xtPhng php ny kh n gin, tuy nhin chi ph tnh ton i vi mi

khng gian dng mu ng vi mi dng mu c sn trn tng ca s kt qu l kh

ln, v cha p ng c vi yu cu thi gian thc i vi vic gim st lp hc.

Hn na, hiu qu ca phng php ny ph thuc hon ton vo bc lc mu tc

trong video, cc dng mu tc c th khng c hnh dng c nh v khp vi

nhng dng mu sn c s b b st trong qu trnh so khp ny.


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Chng 10. Kt lun v hng pht trin10.1 Kt lun

Kha lun ny, vi mc tiu chnh l tm hiu cc k thut pht hin ngi

trong video gim st lp hc. Kha lun t c cc kt qunh sau:

Tm hiu v nh gi cc phng php tr nn khc nhau.

Tm hiu v p dng cc thut ton pht hin mu.

Tm hiu v p dng cc thut ton pht hin mu.

Tm hiu v p dng snake trong vic pht hin phn trn ca ngi

Tm hiu v p dngphng php my hc SVM cng vi c trng

HOG trong vic pht hin phn trn ca ngi

Tuy nhin kha lun cn tn ti cc hn ch:

Tc xl khng t thi gian thc.

H thng cha pht hin c phn trn ca ngi trong cc trng

hp nh: hai phn u ca ngi chm li, phn u ca ngi dnh

vi nhng khu vc c mu ging mu tc.

10.2 Hng pht trinHng pht trin ca kha lun c th l khc phc cc hn ch va nu

trn, c th l:

Ti u xl t thi gian thc.

p dng cc thut ton theo vt i tng c th cho kt qu tt

hn.

Pht hin cc hnh vi trong lp hc nh: pht biu trong lp, ng gt

trong lp


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Ti Liu Tham Kho

[1] Alper Yilmaz, Omar Javed, and Mubarak Shah, "Object Tracking: A Survey,"

pp. 7-15.

[2] Anurag Mittal and Mikos

Documents

Kỹ năng phát hiện vật thể (người) bằng phương pháp trích đặc trưng ảnh HOG và phân lớp sử dụng SVM (Support Vector Machine)