Robust multiple car tracking with occlusion reasoning
ECCV '94 Proceedings of the third European conference on Computer vision (vol. 1)
Real-time Human Motion Analysis by Image Skeletonization
WACV '98 Proceedings of the 4th IEEE Workshop on Applications of Computer Vision (WACV'98)
Robust and Efficient Foreground Analysis for Real-Time Video Surveillance
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Background pixel classification for motion detection in video image sequences
IWANN'03 Proceedings of the Artificial and natural neural networks 7th international conference on Computational methods in neural modeling - Volume 1
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Continuous estimation of signal statistics is an important issue in many video processing systems, such as motion detection in surveillance applications. In this paper we demonstrate how results of classical expressions for variance estimation decrease in accuracy when dealing with sequences containing high illumination variations. The paper also proposes a new estimation method, and shows how, under such conditions, the accuracy of the proposed method produces better results whilst maintaining performance in scenarios with smaller changes, thus improving the motion detection stage of a video surveillance system.