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Effects of Post-processing on Background Subtraction Algorithms
Donovan Parks
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Outline
What is background subtraction?
Project motivation
How is BGS performed and what makes it difficult?
Project goals and results
Concluding remarks
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What is background subtraction?
Real-time method for identifying moving foreground objects within a video
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Project motivation BGS is an important low-level step in
many computer vision applications: Video surveillance Traffic monitoring FG/BG segmentation
My interest is in using BGS to extract human silhouettes for pose estimation
How “good” are the obtained silhouettes in unconstrained environment?
Images from: Sminchisescu and Telea, “Human Pose Estimation from Silhouettes”, 2002.
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How is BGS performed?
Static frame differencing BG model = first frame of video
BGDifferencing
Input Stream
BG Model
Output Masks
Threshold
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What makes BGS difficult?
Moving background elements:
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Adaptive, statistical BG models
BGDifferencing
Mean
Input Stream
BG Model
Output Masks
Threshold
Update BGModel
0 50 100 150 200 2500
0.005
0.01
0.015
0.02
0.025
0.03
Component Value
Pro
babi
lity
0 50 100 150 200 2500
0.005
0.01
0.015
0.02
0.025
0.03
Component Value
Pro
babi
lity
0 50 100 150 200 2500
0.005
0.01
0.015
0.02
0.025
0.03
Component Value
Pro
babi
lity
Variance
Gaussian Pixel Model
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What makes BGS difficult?
Shadows:
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Shadow removal
Shadows have little effect on chromaticity, but reduce luminosity
BGDifferencing
Mean
Input Stream
BG Model
Output Masks
Threshold
Update BGModel
Variance
ShadowRemoval
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What makes BGS difficult?
Ghosting:
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Ghost detection via optical flow
Low optical flow = ghost!
BGDifferencing
Mean
Input Stream
BG Model
Output Masks
Threshold
Update BGModel
Variance
ShadowRemoval
ConnectedComponents
OpticalFlow Test
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What else makes BGS difficult?
FG/BG blending
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Project goals
Evaluate a selection of state-of-the-art background subtraction algorithms Considering 10 algorithms in all
Analyze how post-processing influences the performance of these algorithms Shadow removal Optical flow testing Morphological “cleaning” Area thresholding
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Initial resultsP
reci
sion
Recall
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Example of shadow removal
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Example of “cleaned” results
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Conclusions
Many factors which make BGS difficult
Post-processing can significantly improve results
Results not as “clean” as more computationally expensive approaches
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Questions?
Thank you.