Chinese Journal of Computers   Full Text
Title	A Boosting Discriminative Model for Moving Cast Shadow Detection
Authors	ZHA Yu-Fei1) CHU Ying2) WANG Xun1) MA Shi-Ping1) BI Du-Yan1)
Address	1)(Signal and Information Processing Laboratory, Engineering College of Air Force Engineering University, Xi’an 710038) 2)(Key Laboratory for Image Processing & Intelligent Control, Institute of Pattern Recognition and Artificial Intelligence, Huazhong University of Science and Technology, Wuhan 430074)
Year	2007
Issue	No.8(1295—1301)
Abstract &   Background	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 to eliminate cast shadow on Discriminative Random Fields (DRFs). The method combines different features for Boosting to discriminate cast shadow from moving objects, then temporal and spatial coherence of shadow and foreground are incorporated on Discriminative Random Fields and the problem can be solved by graph cut. Firstly, moving objects are obtained by background subtraction; secondly, shadow candidates can be derived through pre-processing moving objects, in terms of the shadow physical property; thirdly, color information and texture information is derived by comparing shadow and foreground points in current image with corresponding points in background image, which are selected as features for Boosting; finally, temporal and spatial coherence of shadow and foreground is employed on Discriminative Random Fields and discriminate shadow and foreground by graph cut accurately. keywords shadow detection; Boosting; discriminative random fields; graph cut Background The object of this work is detection moving cast shadow in image sequence. Shadow is generated due to a relative absence of light. Moving cast shadow causes serious problem while segmenting and extracting foreground from image sequences, due to the misclassification of moving shadow as foreground. If shadow is misclassified as foreground, it will generate serious error in following processing, for example, tracking, and recognition, etc. Most methods in literatures only employ part features of shadow. What is more, they need experimental thresholds to discriminate foreground and shadow. However, it is difficult in real world. This work 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. The results show that the proposed method can work well in both indoor and outdoor scene. Comparing with the classical methods, the method has good performance both in shadow accurate detection and foreground accurate detection.