5 March 2014 Representing activities with layers of velocity statistics for multiple human action recognition in surveillance applications
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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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Fabio Martínez, Fabio Martínez, Antoine Manzanera, Antoine Manzanera, Eduardo Romero, Eduardo Romero, } "Representing activities with layers of velocity statistics for multiple human action recognition in surveillance applications", Proc. SPIE 9026, Video Surveillance and Transportation Imaging Applications 2014, 90260G (5 March 2014); doi: 10.1117/12.2042588; https://doi.org/10.1117/12.2042588
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