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• W1967456674 abstract "The detection and localization of anomalous behaviors in crowded scenes is considered, and a joint detector of temporal and spatial anomalies is proposed. The proposed detector is based on a video representation that accounts for both appearance and dynamics, using a set of mixture of dynamic textures models. These models are used to implement 1) a center-surround discriminant saliency detector that produces spatial saliency scores, and 2) a model of normal behavior that is learned from training data and produces temporal saliency scores. Spatial and temporal anomaly maps are then defined at multiple spatial scales, by considering the scores of these operators at progressively larger regions of support. The multiscale scores act as potentials of a conditional random field that guarantees global consistency of the anomaly judgments. A data set of densely crowded pedestrian walkways is introduced and used to evaluate the proposed anomaly detector. Experiments on this and other data sets show that the latter achieves state-of-the-art anomaly detection results." @default.
• W1967456674 created "2016-06-24" @default.
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• W1967456674 date "2014-01-01" @default.
• W1967456674 modified "2023-10-13" @default.
• W1967456674 title "Anomaly Detection and Localization in Crowded Scenes" @default.
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• W1967456674 doi "https://doi.org/10.1109/tpami.2013.111" @default.
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• W1967456674 hasPublicationYear "2014" @default.
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