A sub-scene modeling framework for moving cast shadow detection

In this paper, we propose an adaptive and accurate online sub-scene modeling framework for moving cast shadow detection in applications of static-camera video surveillance. To describe shadow appearance more accurately, the proposed method builds adaptive online shadow models for sub-scenes with different conditions of irradiance and reflectance. Additionally, in the correction process, object inner-edges analysis and shadow region expanding are adopted to reject shadow camouflages and recycle the misclassified shadow pixels respectively. The proposed algorithm can adaptively handle the shadow appearance changes and camouflages in both outdoor and indoor scenes without prior information about illuminations and scenarios. Experimental results demonstrate that the proposed method outperforms state-of-the-art methods.