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Journal of Electronic Imaging

Real-time action recognition using a multilayer descriptor with variable size
Author(s): Marlon F. Alcantara; Thierry P. Moreira; Helio Pedrini
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Paper Abstract

Video analysis technology has become less expensive and more powerful in terms of storage resources and resolution capacity, promoting progress in a wide range of applications. Video-based human action detection has been used for several tasks in surveillance environments, such as forensic investigation, patient monitoring, medical training, accident prevention, and traffic monitoring, among others. We present a method for action identification based on adaptive training of a multilayer descriptor applied to a single classifier. Cumulative motion shapes (CMSs) are extracted according to the number of frames present in the video. Each CMS is employed as a self-sufficient layer in the training stage but belongs to the same descriptor. A robust classification is achieved through individual responses of classifiers for each layer, and the dominant result is used as a final outcome. Experiments are conducted on five public datasets (Weizmann, KTH, MuHAVi, IXMAS, and URADL) to demonstrate the effectiveness of the method in terms of accuracy in real time.

Paper Details

Date Published: 3 February 2016
PDF: 9 pages
J. Electron. Imag. 25(1) 013020 doi: 10.1117/1.JEI.25.1.013020
Published in: Journal of Electronic Imaging Volume 25, Issue 1
Show Author Affiliations
Marlon F. Alcantara, Univ. Estadual de Campinas (Brazil)
Thierry P. Moreira, Univ. Estadual de Campinas (Brazil)
Helio Pedrini, Univ. Estadual de Campinas (Brazil)

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