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Representing activities with layers of velocity statistics for multiple human action recognition in surveillance applications

Abstract : A novel action recognition strategy in a video-surveillance context is herein presented. The method starts by computing a multiscale dense optical flow, from which spatial apparent movement regions are clustered as Regions of Interest (RoIs). Each ROI is summarized at each time by an orientation histogram. Then, a multilayer structure dynamically stores the orientation histograms associated to any of the found RoI in the scene and a set of cumulated temporal statistics is used to label that RoI using a previously trained support vector machine model. The method is evaluated using classic human action and public surveillance datasets, with two different tasks: (1) classification of short sequences containing individual actions, and (2) Frame-level recognition of human action in long sequences containing simultaneous actions. The accuracy measurements are: 96.7% (sequence rate) for the classification task, and 95.3% (frame rate) for recognition in surveillance scenes.
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https://hal.archives-ouvertes.fr/hal-01118287
Contributor : Antoine Manzanera <>
Submitted on : Wednesday, February 18, 2015 - 5:04:40 PM
Last modification on : Tuesday, February 11, 2020 - 4:00:03 PM
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  • HAL Id : hal-01118287, version 1

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Fabio Martínez, Antoine Manzanera, Eduardo Romero. Representing activities with layers of velocity statistics for multiple human action recognition in surveillance applications. IS&T/SPIE Electronic Imaging, Feb 2014, San Francisco, United States. ⟨hal-01118287⟩

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