報告人 :林福城指導老師 :陳定宏

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From Res. Center of Intell. Transp. Syst., Beijing Univ. of Technol., Beijing, ChinaBy Zhe Liu ; Yangzhou Chen ; Zhenlong Li Appears in: Computer Science and Information Engineering, 2009 WRI World Congress on

Outline1.Introduction2.Moving object detection 2-1.Conscutive image difference 2-2.Backgrout difference3.Moving object tracking 3-1.Review of Tracking Algorithm 3-2.Camshift Multiple Vehicle Tracking4.Traffic Parameters Estimation5.Experimental Results6.Conclusions

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1.IntroductionTraffic management and information systems: 1.Inductive loop detectors 2.Visual surveillance systems

Our approach specifies three sub process: 1. Vehicle Extraction : Consecutive image difference Background difference 2. Vehicle Tracking 3. Traffic Parameter Estimation

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2.1 Consecutive Image Difference

D(x,y) is the difference image.Mask(x,y) is the image after binarization.

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2.2 Background Difference(1)

It assume a moving objectwould not stay at the same position for more than half of n frames.

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2.2 Background Difference(2)

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2.Moving Object Detection Conclusion 1.Consecutive 2.Background

Easily realized Good

The change of scene luminance

Good

Extract precise Good

Process time Good

After morphological process Equal Equal

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3-1.Review of Tracking Algorithm1.Tracking based on a moving object region:

Size, Color, Shape, Velocity, Centroid2.Tracking based on an active contour of a moving

object3.Tracking based on a moving object model4.Tracking based on selected features of moving

objects : Corner

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3-2.CamShift Multiple Vehicle Tracking

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Mean-Shift Object TrackingGeneral Framework: Target Localization

Search in the model’s neighborhoo

d in next frame

Start from the position of the model

in the current frame

Find best candidate by maximizing a similarity

func.

Repeat the same process

in the next pair of frames

Current frame

… …Model Candidat

e

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Mean-Shift Object TrackingTarget Representation

Choose a reference

target model

Quantized Color Space

Choose a feature space

Represent the model by

its PDF in the feature

space

0

0.05

0.1

0.15

0.2

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0.3

0.35

1 2 3 . . . m

color

Pro

bab

ility

Kernel Based Object Tracking, by Comaniniu, Ramesh, Meer 11

Mean-Shift Object TrackingPDF Representation

,f y f q p y Similarity

Function:

Target Model(centered at 0)

Target Candidate(centered at y)

1..1

1m

u uu mu

q q q

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

1 2 3 . . . m

color

Pro

bab

ility

1..

1

1m

u uu mu

p y p y p

0

0.05

0.1

0.15

0.2

0.25

0.3

1 2 3 . . . m

color

Pro

bab

ility

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4.Traffic Parameters Estimation1.Vehicle count2.Vehicle average speed3.Vehicle size

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Experimental Results

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ConclusionsIn this paper, we have presented methods for

detecting and tracking multiple vehicles in an outdoor environment. Each detected vehicle is assigned a camshift tracker which can effectively track object with different size and shape under different illumination conditions.

The method fails to handle long slow moving vehicle queue.

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