Image Processing Background Elimination in Video Editting
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This document discusses background subtraction methods for comparing current techniques. It provides an overview of common parametric and non-parametric methods from literature. It then describes specific methods implemented at SIG, including single Gaussian, mixture of Gaussians, and a hybrid approach. The document outlines the methodology used to generate ROC curves for evaluating methods on several datasets, including results. It concludes that choosing improvements on the background model affects performance more than the initial model choice.
Image Processing Background Elimination in Video Editting
- 2. Overview ●Problem Statement ●Background Subtraction Methods ●Methodology in creating ROC curve ●Data Sets Used ●ROC Results ●Conclusions/Discussion
- 3. Problem Statement ●Review and compare current methods of Background Subtraction that have been implemented, internally in SIG as well as in Machine Vision community. ●Create a methodology to quantitatively compare these methods of background subtraction. ●Summarize strengths and weakness of methods ●Conclusions / Discussion points going forward
- 4. Methods in Community Gaussian Parametric Models Mixture of Gaussians (MoG) ●Adaptive background mixture models for real-time tracking - C Stauffer, WEL Grimson ●Understanding background mixture models for foreground segmentation - PW Power, JA Schoonees Bimodal ●W 4: real-time surveillance of people and theiractivities - I Haritaoglu, D Harwood, LS Davis Univariate Guassian ●Pfinder: Real-Time Tracking of the Human Body - CR Wren, A Azarbayejani, T Darrell
- 5. Methods in Community Non Parametric Kernel Density Estimation ●Non-parametric model for background subtraction - A Elgammal, D Harwood, L Davis ●Motion-based background subtraction using adaptive kernel density estimation - A Mittal, N Paragios ●Background and Foreground Modeling Using Nonparametric Kernel Density for Visual Surveillance - A Elgammal, R Duraiswami, D Harwood, LS Davis Codebook ●Background modeling and subtraction by codebook construction - K Kim, TH Chalidabhongse, D Harwood, L Davis
- 6. Methods in Community Many other Variations Review Paper ●Background subtraction techniques: a review - M Piccardi Eigenbackground ●Efficient mean-shift background subtraction - T.Jan Conditional Random Fields ●A Dynamic Conditional Random Field Model for Object Segmentation in Image Sequences - Y Wang, Q Ji
- 7. Methods – SIG Implemented ●Single Gaussian (full covariance in RGB) (UNI) currently checked into SVN. ●Mixture of Gaussians (MoG) based on Stauffer and Grimson (loosely). ●Hybrid – modification of UNI with ideas from MOG model (YcRcB, median, clique) ●Multiple Mean Model – Previous SVN check in
- 8. Methodology – Data Sets Duke Data ●Hand ground truth key frames, adaBoost – waiting for code USF Gait Data ●Outdoor gait data, hand ground truth VSSN 2005 Open Source Algorithm Competition ●Outdoor background, sprite foreground Vienna University of Technology, WP2 Evaluation, Integration, and Standards ●Outdoor background, sprite foreground of varying intensities
- 9. Methodology - ROC ●Pd/Pfa calculated over all frames from one clip ●Maximum likelihood of background probability is found at various thresholds ●Probability detection: ratio of ML detected pixels over all pixels in ground truth ●Probability of false alarm: ratio of ML detected pixels not in ground truth over (frame size – ground truth)
- 10. USF Gait Data
- 11. USF Gait Data
- 12. Results – WP2 Data
- 13. Results – WP2 Data
- 14. Results – VSSN Data
- 15. Results – VSSN Data
- 16. Comparison of Methods UNI MoG Hybrid Execution Time 20 fps (>20) 17 fps Memory Requirement M*N*9 M*N*K*4 M*N*3 Variations in Lighting - Adapts Background - Jitter of Bgnd Objects - Compares Clique Compares Clique Bgnd Initialization Average 30 Frames Uses first frame then adapts Median 30 Frames Temporal Correlation - - -
- 17. Conclusion / Discussion ●We have a methodology to compare background subtraction methods. ●Choosing improvements on background model affects performance more than the choice of the initial model itself. ●Method going forward to choose and improve Background Model.