Effective moving cast shadow detection for monocular color traffic image sequences

Abstract

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 (SCS), 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 SCCs. 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 (sunny and cloudy), viewing angles (roadside and overhead), vehicle sizes (small, medium, and large), and colors (similar to the road and not). The results indicate that an average error rate of around 14% is obtained while the lowest error rate is around 3% for large vehicles.