Learning Patterns of Activity Using Real-Time Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detecting Moving Shadows: Algorithms and Evaluation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detecting moving objects, ghosts, and shadows in video streams
IEEE Transactions on Pattern Analysis and Machine Intelligence
Insignificant shadow detection for video segmentation
IEEE Transactions on Circuits and Systems for Video Technology
Skin detection by dual maximization of detectors agreement for video monitoring
Pattern Recognition Letters
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This paper starts from the idea of automatically choosing the appropriate thresholds for a shadow detection algorithm. It is based on the maximization of the agreement between two independent shadow detectors without training data. Firstly, this shadow detection algorithm is described and then, it is adapted to analyze video surveillance sequences. Some modifications are introduced to increase its robustness in generic surveillance scenarios and to reduce its overall computational cost (critical in some video surveillance applications). Experimental results show that the proposed modifications increase the detection reliability as compared to some previous shadow detection algorithms and performs considerably well across a variety of multiple surveillance scenarios.