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Sensors 2013, 13(2), 1635-1650; doi:10.3390/s130201635
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Exploring Techniques for Vision Based Human Activity Recognition: Methods, Systems, and Evaluation

1,2,* , 3,* , 1,2, 1, 1 and 1
1 School of Computer Science and Technology, Wuhan University of Science and Technology, NO. 947 Heping Road, Wuhan 430081, Hubei, China 2 Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System, NO. 1 Huangjiahu West Road, Wuhan 430065, Hubei, China 3 Computer Network and System Administration Program, Michigan Technological University, Houghton, MI 49931, USA
* Authors to whom correspondence should be addressed.
Received: 29 October 2012 / Revised: 20 January 2013 / Accepted: 22 January 2013 / Published: 25 January 2013
(This article belongs to the Section Physical Sensors)
View Full-Text   |   Download PDF [302 KB, 21 June 2014; original version 21 June 2014]   |   Browse Figures

Abstract

With the wide applications of vision based intelligent systems, image and video analysis technologies have attracted the attention of researchers in the computer vision field. In image and video analysis, human activity recognition is an important research direction. By interpreting and understanding human activity, we can recognize and predict the occurrence of crimes and help the police or other agencies react immediately. In the past, a large number of papers have been published on human activity recognition in video and image sequences. In this paper, we provide a comprehensive survey of the recent development of the techniques, including methods, systems, and quantitative evaluation towards the performance of human activity recognition.
Keywords: vision surveillance; activity recognition; surveillance system; performance evaluation vision surveillance; activity recognition; surveillance system; performance evaluation
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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MDPI and ACS Style

Xu, X.; Tang, J.; Zhang, X.; Liu, X.; Zhang, H.; Qiu, Y. Exploring Techniques for Vision Based Human Activity Recognition: Methods, Systems, and Evaluation. Sensors 2013, 13, 1635-1650.

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