document: Effective moving cast shadow detection for monocular color traffic image sequences For an accurate scene analysis using monocular color traffic image sequences , a robust segmentation of moving vehicles from the stationary background is generally required . However , the presence of moving cast shadow may lead to an inaccurate vehicle segmentation , and as a result , may lead to further erroneous scene analysis . We propose an effective method for the detection of moving cast shadow . By observing the characteristics of cast shadow in the luminance , chrominance , gradient density , and geometry domains , a combined probability map , called a shadow confidence score -LRB- SCS -RRB- , is obtained . From the edge map of the input image , each edge pixel is examined to determine whether it belongs to the vehicle region based on its neighboring SCSs . The cast shadow is identified as those regions with high SCSs , which are outside the convex hull of the selected vehicle edge pixels . The proposed method is tested on 100 vehicle images taken under different lighting conditions -LRB- sunny and cloudy -RRB- , viewing angles -LRB- roadside and overhead -RRB- , vehicle sizes -LRB- small , medium , and large -RRB- , and colors -LRB- similar to the road and not -RRB- . The results indicate that an average error rate of around 14 % is obtained while the lowest error rate is around 3 % for large vehicles
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