Pfinder: Real-Time Tracking of the Human Body
IEEE Transactions on Pattern Analysis and Machine Intelligence
Non-parametric Model for Background Subtraction
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
A Bayesian Computer Vision System for Modeling Human Interaction
ICVS '99 Proceedings of the First International Conference on Computer Vision Systems
Effciently Solving Dynamic Markov Random Fields Using Graph Cuts
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Bilayer Segmentation of Live Video
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Smooth foreground-background segmentation for video processing
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part II
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
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This paper proposes an adaptive background model which combines the advantages of both Eigenbackground and pixel-based gaussian models. This method exploits the illumination changes by Eigenbackground. Moreover, it can detect the chroma changes and remove shadow pixels using gaussian models. An adaptively strategy is used to integrate two models. A binary graph cut is used to implement the foreground/background segmentation by developing our data term and smooth term. We validate our method on indoor videos and test it on the benchmark video. Experiments demonstrate our method's efficiency.