Detecting moving shadows: algorithms and evaluation

TLDR

Moving shadows can cause misclassification in dynamic scene analysis, leading to segmentation and tracking errors, yet a comparative evaluation of existing detection algorithms is still missing. The paper surveys moving shadow detection methods and provides a comparative empirical evaluation of representative algorithms. The authors categorize algorithms into four classes—two statistical and two deterministic—and introduce quantitative and qualitative metrics to evaluate them on indoor and outdoor video benchmarks. The authors release a benchmark suite of indoor and outdoor video sequences with ground‑truth data to facilitate further research.

Abstract

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, we present a comprehensive survey of moving shadow detection approaches. We organize contributions reported in the literature in four classes two of them are statistical and two are deterministic. We also present a comparative empirical evaluation of representative algorithms selected from these four classes. Novel quantitative (detection and discrimination rate) 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/aton/shadow to allow for others in the community to experiment with new algorithms and metrics.