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Communication Dans Un Congrès Année : 2014

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

Fabio Martínez
  • Fonction : Auteur
Antoine Manzanera
Eduardo Romero
  • Fonction : Auteur

Résumé

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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Dates et versions

hal-01118287 , version 1 (18-02-2015)

Identifiants

  • 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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