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Real-time clothing recognition in surveillance videos

Recognition of clothing categories from videos is appealing to emerging applications such as intelligent customer profile analysis and computer-aided fashion design. This paper presents a complete system to tag clothing categories in real-time, which addresses some practical complications in surveillance videos. Specifically, we take advantage of face detection and tracking to locate human figures and develop an efficient clothing segmentation method utilizing Voronoi images to select seeds for region growing. We compare clothing representations combining color histograms and 3 different texture descriptors. Evaluated on a video dataset with 937 persons and 25441 cloth instances, the system demonstrates promising results in recognizing 8 clothing categories.