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Background extraction with a coarse to fine approach
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Outline Pixel approach Block-based approach Gaussian mixture model
Algorithm description Block-based approach Two level classification Experimental results
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Background extraction constraints
Depend on the application Real-time Real environment Shadows Illumination changes Background object displacements …
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Outline Pixel approach Block-based approach Gaussian mixture model
Algorithm description Block-based approach Two level classification Experimental results
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Gaussian mixture model [Stauffer & Grimson, ’99]
Color at each pixel location is described by a mixture of K Gaussians. Mean, variance and relative weight of each Gaussian are updated according to observations and a learning rate. Good model for illumination changes.
![Gaussian mixture model [Stauffer & Grimson, ’99]](http://slideplayer.com./slide/16049293/88/images/5/Gaussian+mixture+model+%5BStauffer+%26+Grimson%2C+%E2%80%9999%5D.jpg "Color at each pixel location is described by a mixture of K Gaussians. Mean, variance and relative weight of each Gaussian are updated according to observations and a learning rate. Good model for illumination changes.")
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R-D Optimized Policy (1) [Chou & Miao, ’01]
Algorithm description [Harville et al., ’01] R-D Optimized Policy (1) [Chou & Miao, ’01] Work in YUV space (reduce shadow misclassification). Determine for each pixel the Gaussian that matches its value best. (on-line K-means approximation). Threshold criterion is used to determine if the matched Gaussian corresponds to the background.
![R-D Optimized Policy (1) [Chou & Miao, ’01]](http://slideplayer.com./slide/16049293/88/images/6/R-D+Optimized+Policy+%281%29+%5BChou+%26+Miao%2C+%E2%80%9901%5D.jpg "Algorithm description [Harville et al., ’01] R-D Optimized Policy (1) [Chou & Miao, ’01] Work in YUV space (reduce shadow misclassification). Determine for each pixel the Gaussian that matches its value best. (on-line K-means approximation). Threshold criterion is used to determine if the matched Gaussian corresponds to the background.")
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Outline Pixel approach Block-based approach Gaussian mixture model
Algorithm description Block-based approach Two level classification Experimental results
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R-D Optimized Policy (1) [Chou & Miao, ’01]
Two level classification R-D Optimized Policy (1) [Chou & Miao, ’01] First, classify at the block level using a proportion criterion. No parameter updates at this point. Second, use the pixel approach for the regions not classified as background after the first step.
![R-D Optimized Policy (1) [Chou & Miao, ’01]](http://slideplayer.com./slide/16049293/88/images/8/R-D+Optimized+Policy+%281%29+%5BChou+%26+Miao%2C+%E2%80%9901%5D.jpg "Two level classification. R-D Optimized Policy (1) [Chou & Miao, ’01] First, classify at the block level using a proportion criterion. No parameter updates at this point. Second, use the pixel approach for the regions not classified as background after the first step.")
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Experimental results Typical values: K= 3,
Initial variance = 100, Threshold = 0.4, Alpha (learning rate) = 0.006, Block size = 16, Threshold = Block size.
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Conclusion Current block based approach can be faster with the same visual quality. Future work could “dissociate” pixel level and block level completely. Still an open problem.
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Acknowledgments Michael Harville Susie Wee John Apostolopoulos
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Background extraction with a coarse to fine approach
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