Non-parametric Model for Background Subtraction
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
Learning a Sparse, Corner-Based Representation for Time-varying Background Modeling
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Efficient hierarchical method for background subtraction
Pattern Recognition
Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
Motion-based background subtraction using adaptive kernel density estimation
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
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Pan Tilt Zoom cameras have the ability to cover wide areas with an adapted resolution. Since the logical downside of high resolution is a limited field of view, a guard tour can be used to monitor a large scene of interest. However, this greatly increases the duration between frames associated to a specific location. This constraint makes most background algorithms ineffective. In this article we propose a background subtraction algorithm suitable to cameras with very low frame rate. Its main interest consists in the resulting robustness to sudden illumination changes. The background model which describes a wide scene of interest consisting of a collection of images can thus be successfully maintained. This algorithm is compared with the state of the art and a discussion regarding its properties follows.