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Effects of Post-processing on Background Subtraction Algorithms

Explore the effects of post-processing on background subtraction algorithms, focusing on shadow removal, optical flow testing, and morphological cleaning to improve results. Evaluate 10 algorithms and discuss the challenges faced in BGS. Initial results and conclusions provided.

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Effects of Post-processing on Background Subtraction Algorithms

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  1. Effects of Post-processing on Background Subtraction Algorithms Donovan Parks

  2. Outline • What is background subtraction? • Project motivation • How is BGS performed and what makes it difficult? • Project goals and results • Concluding remarks

  3. What is background subtraction? • Real-time method for identifying moving foreground objects within a video

  4. 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.

  5. How is BGS performed? • Static frame differencing • BG model = first frame of video

  6. What makes BGS difficult? • Moving background elements:

  7. Adaptive, statistical BG models

  8. What makes BGS difficult? • Shadows:

  9. Shadow removal • Shadows have little effect on chromaticity, but reduce luminosity

  10. What makes BGS difficult? • Ghosting:

  11. Ghost detection via optical flow • Low optical flow = ghost!

  12. What else makes BGS difficult? • FG/BG blending

  13. 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

  14. Initial results

  15. Example of shadow removal

  16. Example of “cleaned” results

  17. Conclusions • Many factors which make BGS difficult • Post-processing can significantly improve results • Results not as “clean” as more computationally expensive approaches

  18. Questions? Thank you.

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