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Detecting Moving Objects, Ghosts, and Shadows in Video Streams
October 2003 (vol. 25 no. 10)
pp. 1337-1342

Abstract—Background subtraction methods are widely exploited for moving object detection in videos in many applications, such as traffic monitoring, human motion capture, and video surveillance. How to correctly and efficiently model and update the background model and how to deal with shadows are two of the most distinguishing and challenging aspects of such approaches. This work proposes a general-purpose method that combines statistical assumptions with the object-level knowledge of moving objects, apparent objects (ghosts), and shadows acquired in the processing of the previous frames. Pixels belonging to moving objects, ghosts, and shadows are processed differently in order to supply an object-based selective update. The proposed approach exploits color information for both background subtraction and shadow detection to improve object segmentation and background update. The approach proves fast, flexible, and precise in terms of both pixel accuracy and reactivity to background changes.

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Index Terms:
Background modeling, color segmentation, reactivity to changes, shadow detection, video surveillance, object-level knowledge.
Citation:
Rita Cucchiara, Costantino Grana, Massimo Piccardi, Andrea Prati, "Detecting Moving Objects, Ghosts, and Shadows in Video Streams," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, no. 10, pp. 1337-1342, Oct. 2003, doi:10.1109/TPAMI.2003.1233909
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