8/10/2019 SPIE_EI_9026_2014_Poster
1/1
Optical flow based Kalman tracker for body joint tracking using
HOG-LBP matching
Block Schematic
Kalman Correction Stage
Combination of optical flow estimate,
region-based LBP-HOG matching.
Proposed Methodology
Region Descriptor
matching for points
within search region
Region
Match
found)?
OptFlowin
search
region?
Set estimate as Yes
No
Yes
No
Results and Analysis
Compute the spatio-temporal discrepancy between estimated and
true
trajectory.
Distance Measure :
, = (log , 2
Conclusions
Finer estimates of joints tracks.
Will aid in gesture recognition.
Get accurate gait signatures.
Future Work
Use of automatic human pose
estimation.
Incorporate graph model
for inter-joint relationships.
Results, Analysis and Conclusions
Objectives
To identify and track specific joints for real-time
processing.
To develop a tracking mechanism for use with pose
estimation.
To use on surveillance scenario or feed from CCTV cameras.
Challenges
Very low resolution imagery.
Noisy artifacts such as interlacing effects.
Applications
Gesture recognition and Gait analysis.
Proposed Solution Use of primitive techniques : optical flow,
region based matching.
Introduction Joint Tracking Illustrations
Shoulder Trajectory Elbow Trajectory Wrist Trajectory
Tracked Points
Manually
Annotated Points
Manual Points
Fitted with
polynomial of
Degree 2
Manual Points
Fitter with
polynomial ofDegree 3
Estimated Region 2 Tracked Points 2 Estimated Region 3 Tracked
Points 3
Input Video
Learn Background
Model
Foreground
Segmentation
Read
Frame
Compute Global
Velocity(Using LK)
Compute Global
Velocity(Using LK)
Initialize Joint
Descriptor and Joint
Tracker
Manually Annotated
Points/Automatic Pose
Estimation
Estimate joint
location in current
frame using LK
Optical Flow
Initial
Frames?
KalmanFilter Correction (MeasurementUpdate Update)
KalmanPrediction (Time Update)
Compute Elliptical Search Region
Yes
No
Binu M Nair
PhD Student
Electrical and Computer
Engineering
[email protected]
