Fuzzy system based human behavior recognition by combining behavior prediction and recognition |
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| Affiliation: | 1. Department of electronics and information engineering, Korea University Sejong Campus, Sejong 30019, Korea\n;2. Department of control and robotics engineering, Kunsan National University, Kunsan 54150, Korea;1. School of Management, Kyung Hee University, 26, Kyungheedae-ro, Dongdaemun-gu, Seoul 130-701, Republic of Korea;2. Big Data Center at Kyung Hee University 26, Kyungheedae-ro, Dongdaemun-gu, Seoul 130-701, Republic of Korea;1. Graduate School of Ecnonomics, University of Tokyo, 7-3-1 Hongo Bunkyo-ku, Tokyo, 113-0033, Japan;2. Faculty of Economics, University of Tokyo, 7-3-1 Hongo Bunkyo-ku, Tokyo, 113-0033, Japan;1. Warsaw University of Life Sciences, 02-787 Warsaw, Nowoursynowska 166, Poland;2. Warsaw University of Technology, Faculty of Electrical Engineering, Koszykowa 75, Warsaw, Poland;3. Military University of Technology, Faculty of Electronics, Kaliskiego 2, 00-908 Warsaw, Poland;4. Memorial Cancer Centre and Institute of Oncology, Roentgena 5, 02-781 Warsaw, Poland;5. Research Team on Intelligent Systems in Imaging and Artificial Vision (SIIVA), RIADI Laboratory, ISI, 2 Street Abou Rayhane Bayrouni, 2080 Ariana, Tunisia |
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| Abstract: | With the development of intelligent surveillance systems, human behavior recognition has been extensively researched. Most of the previous methods recognized human behavior based on spatial and temporal features from (current) input image sequences, without the behavior prediction from previously recognized behaviors. Considering an example of behavior prediction, “punching” is more probable in the current frame when the previous behavior is “standing” as compared to the previous behavior being “lying down.” Nevertheless, there has been little study regarding the combination of currently recognized behavior information with behavior prediction. Therefore, we propose a fuzzy system based behavior recognition technique by combining both behavior prediction and recognition. To perform behavior recognition during daytime and nighttime, a dual camera system of visible light and thermal (far infrared light) cameras is used to capture 12 datasets including 11 different human behaviors in various surveillance environments. Experimental results along with the collected datasets and open database showed that the proposed method achieved higher accuracy of behavior recognition when compared to conventional methods. |
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