Efficient background subtraction and shadow removal for monochromatic video sequences
IEEE Transactions on Multimedia - Special section on communities and media computing
Shadows Removal by Edges Matching
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part II
Edge-Based Algorithm for Shadows and Ghosts Removing
PCM '09 Proceedings of the 10th Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
A shadow detection method for remote sensing images using affinity propagation algorithm
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Adaptive shadow estimator for removing shadow of moving object
Computer Vision and Image Understanding
An efficient and robust moving shadow removal algorithm and its applications in ITS
EURASIP Journal on Advances in Signal Processing - Special issue on advanced image processing for defense and security applications
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part III
Shadow detection: A survey and comparative evaluation of recent methods
Pattern Recognition
Texture and space-time based moving objects segmentation and shadow removing
ICSI'12 Proceedings of the Third international conference on Advances in Swarm Intelligence - Volume Part II
Towards unsupervised semantic segmentation of street scenes from motion cues
Proceedings of the 27th Conference on Image and Vision Computing New Zealand
Journal of Real-Time Image Processing
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Moving objects segmentation plays a very important role in real-time image analysis. However, as one of the common parts in the natural scenes, shadows severely interfere with the accuracy of moving objects detection in video surveillance. In this paper, we present a novel method for moving cast shadows detection. Based on the analysis of the physical model of moving shadows, we prove that the ratio edge is illumination invariant. The distribution of the ratio edge is discussed and a significance test is performed to classify each moving pixel into foreground object or moving shadow. Intensity constraint and geometric heuristics are imposed to further improve the performance. Experiments on various typical scenes exhibit the robustness of the proposed method. Extensively quantitative evaluation and comparison demonstrate that the proposed method significantly outperforms state-of-the-art methods.