Shadow detection: A survey and comparative evaluation of recent methods
Pattern Recognition
Journal of Real-Time Image Processing
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Moving cast shadow removal is an important yet difficult problem in video analysis and applications. This paper presents a novel algorithm for detection of moving cast shadows, that based on a local texture descriptor called Scale Invariant Local Ternary Pattern (SILTP). An assumption is made that the texture properties of cast shadows bears similar patterns to those of the background beneath them. The likelihood of cast shadows is derived using information in both color and texture. An online learning scheme is employed to update the shadow model adaptively. Finally, the posterior probability of cast shadow region is formulated by further incorporating prior contextual constrains using a Markov Random Field (MRF) model. The optimal solution is found using graph cuts. Experimental results tested on various scenes demonstrate the robustness of the algorithm.