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Image difference threshold strategies and shadow detection
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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Shadows Removal by Edges Matching
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Adaptive shadow estimator for removing shadow of moving object
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Shadow detection: A survey and comparative evaluation of recent methods
Pattern Recognition
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A survey of cast shadow detection algorithms
Pattern Recognition Letters
Robust decentralized multi-model adaptive template tracking
Pattern Recognition
A novel moving cast shadow detection of vehicles in traffic scene
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Computer Vision and Image Understanding
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This paper presents a new approach for shadow detection of moving objects in visual surveillance environment, improving localization, segmentation, tracking and classification of detected objects. An automatic segmentation procedure based on adaptive background difference is performed in order to detect potential shadow points so that, for all moving pixels, the approach evaluates the compatibility of photometric properties with shadow characteristics. The shadow detection approach is improved by evaluating the similarity between little textured patches, since shadow regions present same textural characteristics in each frame and in the corresponding adaptive background model. In this work we suggest a new approach to describe textural information in terms of redundant systems of functions. The algorithm is designed to be unaffected by scene type, background type or light conditions. Experimental results validate the algorithm's performance on a benchmark suite of indoor and outdoor video sequences.