W4: Real-Time Surveillance of People and Their Activities
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detection and Location of People in Video Images Using Adaptive Fusion of Color and Edge Information
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 4
ACM SIGGRAPH 2004 Papers
An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Better Foreground Segmentation for Static Cameras via New Energy Form and Dynamic Graph-cut
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
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Foreground detection is essential for semantic understanding and discovery for surveillance videos but still suffers from inefficiency and poor shape or silhouette detection. We argue to leverage multiple modalities (e.g., color appearance, foreground likelihood, spatial continuity, etc.) for foreground detection and propose a rigorous fusion method by graph cut. We further devise three strategies (e.g., dividing the graph cut problem into several subtasks, exploiting multi-core platform, etc.) to speed up the detection. Experimenting in open benchmarks, the proposed method outperforms other rival approaches in terms of detection accuracy and frame rate.