robust object tracking in crowd dynamic scenes using ...cli53/papers/chi_accv12_slides.pdf · svm...
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Chi Li, Le Lu, Gregory D. Hager, Hanzi Wang
2013/1/13 Johns Hopkins University
Robust Object Tracking in Crowd Dynamic Scenes using Explicit Stereo Depth
The Challenges of Object Trackingin Dynamic Scenes
Drifting Limitation of the appearance model
Sharp & Irregular Model Change Motion Field Appearance Feature Space
Partial and Complete Occlusion Occlusion Detection (Hard!) Object Reacquisition
2013/1/13 Johns Hopkins University
What if depth as the main cue?
Intuition: Powerful for background subtraction
Stable under sharp or irregular model change
Reliable indicator of occlusion detection
2013/1/13 Johns Hopkins University
Previous Work using Depth
Depth-assisted Tracking Ground plane & Odometry Depth-assisted detection
Multi-view Tracking Cannot be applied in dynamic scene
Kinect-based Human Tracking Limited Depth-of-Field Hard to be extended to arbitrary object
2013/1/13 Johns Hopkins University
Depth-driven Tracking Framework
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Depth Estimation
Color Clustering
Superpixel Classification Dominant
Depth Group
SVM Shape Filter+
Space-Color Histogram
Foreground
Occlusion Handling
Under Occlusion?Occlusion Detection
Reacquisition OB Tracking
no
yesReset Tracker
Next Fram
e
no
noyesyes
Depth Pixel Clustering
Depth BlobSegmentation
Object Hypotheses Generation
SVM Vertical Shape Filter
Space-Color Histogram
Occlusion Handling
2013/1/13 Johns Hopkins University
Depth pixel clustering in (X Y D) space
Meanshift Clustering
Dominant Depth Group
Depth Pixel Clustering
Depth BlobSegmentation
Object Hypotheses Generation
SVM Vertical Shape Filter
Space-Color Histogram
Occlusion Handling
Dominant Depth Blob Segmentation
Superpixel Classification: If more than 60% of pixels in on superpixel belong
to the dominant depth group, we classify this superpixel into the dominant depth blob.
2013/1/13 Johns Hopkins University
Depth Pixel Clustering
Depth BlobSegmentation
Object Hypotheses Generation
SVM Vertical Shape Filter
Space-Color Histogram
Occlusion Handling
2013/1/13 Johns Hopkins University
Dominant depth blob splitting based on vertical shape distribution
……
Projection on X axis
Object Hypotheses
Depth Pixel Clustering
Depth BlobSegmentation
Object Hypotheses Generation
SVM Vertical Shape Filter
Space-Color Histogram
Occlusion Handling
2013/1/13 Johns Hopkins University
SVM vertical shape filter
Object Hypotheses
……
Blobs After filtering
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Vertical Feature Extraction
SVM Vertical Shape Filter
Single Object Blobs
GaussianFiltering
Depth Pixel Clustering
Depth BlobSegmentation
Object Hypotheses Generation
SVM Vertical Shape Filter
Space-Color Histogram
Occlusion Handling
2013/1/13 Johns Hopkins University
SVM shape filter training Positive Sample: Manually labeled foreground mask Negative Sample: Manually labeled + noisy mask
from depth segmentation including 2, 3 and background + object.
After morphological filtering, we project pixels only above the centroid of the depth blob.
Apply interpolation and median filter to adjust the dimension of vertical feature to the same length
Depth Pixel Clustering
Depth BlobSegmentation
Object Hypotheses Generation
SVM Vertical Shape Filter
Space-Color Histogram
Occlusion Handling
2013/1/13 Johns Hopkins University
Choose candidate based on color distribution
Space-color Histogram
Similarity Score
1 N
. . . . . .
Concatenation of the histograms of different regions
Depth Pixel Clustering
Depth BlobSegmentation
Object Hypotheses Generation
SVM Vertical Shape Filter
Space-Color Histogram
Occlusion Handling
Occlusion Detection Object Reacquisition
2013/1/13 Johns Hopkins University
Experiment1——Comparison
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Tracking Error
Comparison Example
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Tracking Error CurvatureExperiment1——Comparison
Experiment2——Depth Vs. Appearance
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Experiment3——Figure Recovery
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Experiment3——Figure Recovery
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Experiment3——Figure Recovery
2013/1/13 Johns Hopkins University
2013/1/13 Johns Hopkins University
Experiment4—Successive Occlusion Handling
253# 258#
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271#277#
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Experiment5——Failure Case1
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Experiment6——Failure Case2
Any Questions?
2013/1/13 Johns Hopkins University
Thank you for listening!
2013/1/13 Johns Hopkins University