CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Fast Lighting Independent Background Subtraction
International Journal of Computer Vision - Special issue on a special section on visual surveillance
On sequential Monte Carlo sampling methods for Bayesian filtering
Statistics and Computing
W4S: A real-time system detecting and tracking people in 2 1/2D
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
Exemplar-Based Face Recognition from Video
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Identification of humans using gait
IEEE Transactions on Image Processing
A survey of advances in vision-based human motion capture and analysis
Computer Vision and Image Understanding - Special issue on modeling people: Vision-based understanding of a person's shape, appearance, movement, and behaviour
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Usually, background subtraction is approached as a pixel-based process, and the output is (a possibly thresholded) image where each pixel reflects, independent from its neighboring pixels, the likelihood of itself belonging to a foreground object. What is neglected for better output is the correlation between pixels. In this paper we introduce a model-based background subtraction approach which facilitates prior knowledge of pixel correlations for clearer and better results. Model knowledge is being learned from good training video data, the data is stored for fast access in a hierarchical manner. Bayesian propagation over time is used for proper model selection and tracking during model-based background subtraction. Bayes propagation is attractive in our application as it allows to deal with uncertainties during tracking. We have tested our approach on suitable outdoor video data.