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http://dx.doi.org/10.9717/kmms.2017.20.2.196

A Study on Efficient Learning Units for Behavior-Recognition of People in Video  

Kwon, Ick-Hwan (Electrics and Telecommunications Research Institute)
Hadjer, Boubenna (Dept. of Electrical and Computer Engineering, Graduate School, Pusan National University)
Lee, Dohoon (Dept. of Electrical and Computer Science Engineering, Pusan National University)
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Abstract
Behavior of intelligent video surveillance system is recognized by analyzing the pattern of the object of interest by using the frame information of video inputted from the camera and analyzes the behavior. Detection of object's certain behaviors in the crowd has become a critical problem because in the event of terror strikes. Recognition of object's certain behaviors is an important but difficult problem in the area of computer vision. As the realization of big data utilizing machine learning, data mining techniques, the amount of video through the CCTV, Smart-phone and Drone's video has increased dramatically. In this paper, we propose a multiple-sliding window method to recognize the cumulative change as one piece in order to improve the accuracy of the recognition. The experimental results demonstrated the method was robust and efficient learning units in the classification of certain behaviors.
Keywords
Frame Learning Units; Behavior Recognition; Multiple Support Vector Machine; Multi Sliding Window;
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Times Cited By KSCI : 3  (Citation Analysis)
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