A boosting discriminative model for moving cast shadow detection

Authors:
Yufei Zha;Ying Chu;Duyan Bi
Affiliations:
Signal and Information Processing Lab, Engineering College of Air Force Engineering University, Xi'an, China;Institute for Pattern Recognition and Artificial Intelligence, Huazhong University of Science and Technology, China;Signal and Information Processing Lab, Engineering College of Air Force Engineering University, Xi'an, China
Venue:
EMMCVPR'07 Proceedings of the 6th international conference on Energy minimization methods in computer vision and pattern recognition
Year:
2007

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Abstract

Moving cast shadow causes serious problem while segmenting and extracting foreground from image sequences, due to the misclassification of moving shadow as foreground. This paper proposes a boosting discriminative model for moving cast shadow detection. Firstly, color invariance subspace and texture invariance subspace are obtained by the color and texture difference between current image and background image; then, boosting is selected based on theses subspaces to discriminate cast shadow from moving objects; finally, temporal and spatial coherence of shadow and foreground is employed on Discriminative Random Fields for accurate image segmentation through graph cut. Results show that the proposed method excels classical method both in indoor and outdoor scene.