Comparative Evaluation of Moving Shadow Detection Algorithms

Moving shadows need careful consideration in the development of robust dynamic scene analysis systems. Moving shadow detection is critical for accurate object detection in video streams, since shadow points are often misclassified as object points causing errors in segmentation and tracking. Many algorithms have been proposed in the literature that deal with shadows. However, a comparative evaluation of the existing approaches is still lacking. In this paper, the full range of problems underlying the shadow detection are identified and discussed. We present a comprehensive survey of moving shadow detection approaches. We organize contributions reported in the literature in four classes. We also present a comparative empirical evaluation of representative algorithms selected from these four classes. Quantitative (detection and discrimination accuracy) and qualitative metrics (scene and object independence, flexibility to shadow situations and robustness to noise) are proposed to evaluate these classes of algorithms on a benchmark suite of indoor and outdoor video sequences. These video sequences and associated “ground-truth” data are made available at http://cvrr.ucsd.edu:88/aton/shadow to allow for others in the community to experiment with new algorithms and metrics. Keywords— Shadow detection, performance evaluation, dynamic scene analysis, object detection, tracking, segmentation, traffic scene analysis, visual surveillance