Next Article in Journal
Previous Article in Journal
Sensors 2013, 13(2), 1635-1650; doi:10.3390/s130201635
Review

Exploring Techniques for Vision Based Human Activity Recognition: Methods, Systems, and Evaluation

1,2,* , 3,* , 1,2, 1, 1 and 1
Received: 29 October 2012; in revised form: 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, updated 21 June 2014; original version uploaded 21 June 2014]
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.

Export to BibTeX |
EndNote


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.

AMA 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(2):1635-1650.

Chicago/Turabian Style

Xu, Xin; Tang, Jinshan; Zhang, Xiaolong; Liu, Xiaoming; Zhang, Hong; Qiu, Yimin. 2013. "Exploring Techniques for Vision Based Human Activity Recognition: Methods, Systems, and Evaluation." Sensors 13, no. 2: 1635-1650.


Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert