Fundamenta morphologicae mathematicae
Fundamenta Informaticae - Special issue on mathematical morphology
Cast shadow segmentation using invariant color features
Computer Vision and Image Understanding
Cast shadow detection in video segmentation
Pattern Recognition Letters
Face detection using quantized skin color regions merging andwavelet packet analysis
IEEE Transactions on Multimedia
A highly efficient system for automatic face region detection in MPEG video
IEEE Transactions on Circuits and Systems for Video Technology
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
An Embedded Real-Time Surveillance System: Implementation and Evaluation
Journal of Signal Processing Systems
A hardware architecture for real-time video segmentation utilizing memory reduction techniques
IEEE Transactions on Circuits and Systems for Video Technology
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To efficiently classify and track video objects in a surveillance application, it is essential to reduce the amount of streaming data. One solution is to segment the video into background, i.e. stationary objects, and foreground, i.e. moving objects, and then discard the background. One such motion segmentation algorithm that has proven reliable is the Stauffer and Grimson algorithm. This paper investigates how different color spaces affect the segmentation result in terms of noise and shadow sensitivity. Shadows are especially problematic since they not only distort shape but can also result in falsely connected objects that will complicate tracking and classification. Therefore, a new decision kernel for the segmentation algorithm is presented. This kernel alters the probability of foreground detection to reduce shadows and to increase the chance of correct segmentation for objects with a skin tone color, e.g. faces.