Real-time action recognition using a multilayer descriptor with variable size

Marlon F. Alcantara; Thierry P. Moreira; Helio Pedrini

doi:10.1117/1.JEI.25.1.013020

3 February 2016 Real-time action recognition using a multilayer descriptor with variable size

Marlon F. Alcantara, Thierry P. Moreira, Helio Pedrini

Author Affiliations +

Journal of Electronic Imaging, Vol. 25, Issue 1, 013020 (February 2016). https://doi.org/10.1117/1.JEI.25.1.013020

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.

Citation Download Citation

Marlon F. Alcantara, Thierry P. Moreira, and Helio Pedrini "Real-time action recognition using a multilayer descriptor with variable size," Journal of Electronic Imaging 25(1), 013020 (3 February 2016). https://doi.org/10.1117/1.JEI.25.1.013020

Published: 3 February 2016

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