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• W2057285138 abstract "This paper addresses the problem of detecting and localizing abnormal activities in crowded scenes. A spatiotemporal Laplacian eigenmap method is proposed to extract different crowd activities from videos. This is achieved by learning the spatial and temporal variations of local motions in an embedded space. We employ representatives of different activities to construct the model which characterizes the regular behavior of a crowd. This model of regular crowd behavior allows the detection of abnormal crowd activities both in local and global contexts and the localization of regions which show abnormal behavior. Experiments on the recently published data sets show that the proposed method achieves comparable results with the state-of-the-art methods without sacrificing computational simplicity." @default.
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• W2057285138 date "2013-12-01" @default.
• W2057285138 modified "2023-09-23" @default.
• W2057285138 title "Laplacian Eigenmap With Temporal Constraints for Local Abnormality Detection in Crowded Scenes" @default.
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• W2057285138 doi "https://doi.org/10.1109/tcyb.2013.2242059" @default.
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