A model based method of pedestrian abnormal behavior detection in traffic scene

2015 IEEE 1st International Smart Cities Conference, ISC2 2015

Volumn , Issue , 2015, Pages

  Qianyin, Jiang ^a,b,c   Guoming, Li ^a,b,c   Jinwei, Yu ^a,b   Xiying, Li ^a,b,c  

^a SUN YAT SEN UNIVERSITY   (China)

^b Guangdong Provincial Key Laboratory of Intelligent Transportation System   (China)

^c Key Laboratory of Video and Image Intelligent Analysis and Application Technology of MPS   (China)

Author keywords

pedestrian abnormal behavior detection; pedestrian abnormal behavior model; pedestrian detection; pedestrian tracking; traffic surveillance video

Indexed keywords

IMAGE PROCESSING; MOTOR TRANSPORTATION; OBJECT DETECTION; SECURITY SYSTEMS; TRACKING (POSITION); TRAFFIC CONTROL; TRAJECTORIES; TRANSPORTATION;

ABNORMAL BEHAVIOR; ABNORMAL BEHAVIOR DETECTIONS; PEDESTRIAN DETECTION; PEDESTRIAN TRACKING; TRAFFIC SURVEILLANCE;

ROADS AND STREETS;

EID: 84969972219     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ISC2.2015.7366164     Document Type: Conference Paper

References (12)

1. MASOUD O, PAPANIKOLOPOULOS N. A method for human action recognition. Image and Vision Computing, 2003, 21 (8): 729-743.
2. Yin Yong, Wang Jiandong, and Jin Xiangang. Human Abnormal activity recognition for approximate period motion. Computer Engineering and Applications, 2010, 46 (26): 158-161.
3. Dong-Gyu Lee, Heung-Il Suk and Seong-Whan Lee. Modeling crowd motions for abnormal activity detection. 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 325-330, 2014.
4. Zhicheng Wang, and Jun Zhang. Detecting Pedestrian abnormal behavior basedonfuzzy associative memory. Fourth International Conference on Natural Computation, vol. 6, pp. 143-147, 2008.
5. BREGLER C. Learning and recognizing human dynamics in video sequences. Proc of IEEE Conference on Computer Vision and Pattern Recognition. 1997: 568-574.
6. Zhou Yibo, He Xiaohai, Zhang Shengjun, and Qing Linbo. A new detection algorithm of abnormal behavior. Computer Engineering and Application, 2012, 48 (3): 192-194.
7. Zhang Lijun, Wu Xiaojuan, Sheng Zan, and Qi Lei. Behabior recognition method in complex environment using HMM. Computer Engineering, 2008, 34 (7): 212-214.
8. P. L. M. Bouttefroy, A. Beghdadi, A. Bouzerdoum, andS. L. Phung. Markov random fields for abnormal behavior detection on highways. Visual Information Processing (EUVIP), 2010 2nd European Workshop on, pp. 149-154, July, 2010.
9. Shen-Bin Hsu, Chin-Chuan Han, Cheng-Ta Hsieh, and Kuo-Chin Fan. Falling and slipping detection for pedestrians using a manifold learning approach. Proceedings of the 2013 International Conference on Machine Learning and Cyberntics, 14-17 July, 2013.
10. Wang Zhengqin, and Liu Fuqiang. Comparison of adaptive background extraction algorithm of Object Scene. Computer Engineering, 2008. 34 (23): 220-223.
11. Li Xi, Cao Guangyi, and Fu Xiaowei. The pedestrian tracking counting method based on real-time imagesequence. Computer Simulation. 2005, 22 (2): 79-81.
12. Zhao Youting, LiXiying, and Luo Donghua. Based on video vehicle trajectory model of traffic incident automatic detection method research. Journal of Sun Yansen University, 2011, 50 (4): 56-60.