Study of Multi Target Tracking Algorithm Based on CRF Model

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Moving target detection and tracking in complex background is the key technology in the field of computer vision, which has become one of the focus researches for many scholars at home and abroad. Many applications, such as robot navigation, video tracking, are closely related with the moving object detection and tracking in complex background. In this paper, we improve the traditional stochastic model and target matching algorithm, combining with the feature optical flow method, to detect and track moving target detection in complex scene, and get online modified CRF model. It provides theoretical support and guidance technology for future research.

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3860-3863

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May 2014

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© 2014 Trans Tech Publications Ltd. All Rights Reserved

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[1] Cen Feng, Qi Feihu. Geodesic active contour tracking algorithm research - used in complex background and non rigid moving object tracking [J]. Computer research and development, 2011, 40 (2): 283-288.

Google Scholar

[2] X. Ren, C. Fowlkes, J. Malik. Cue integration for figure/ground labeling [C]. Proc. Neural. Information Processing Systems (NIPS), 2010: 1121-1128.

Google Scholar

[3] S. Kumar, M. Hebert. Discriminative random fields: a discriminative framework for contextual interaction in classification [C]. Proc. IEEE Int. Conf. Computer Vision, 2011: 1150-1159.

DOI: 10.1109/iccv.2003.1238478

Google Scholar

[4] Jiang Shuhong, Wang Qin, Zhang Jianqiu, et al. Tracking algorithm based on center distance of target weighting and image feature recognition [J]. Journal of electronic, 2011, 34(7): 1175-1180.

Google Scholar

[5] Hou Zhiqiang, Han Chongzhao. A survey of visual tracking [J]. Journal of automatic, 2012, 32(4): 603-617.

Google Scholar

[6] KoS, LeeS, Jeon S et a1. Fast digital image stabilizer based on gray-code bit-plane matching IEEE Trans [J]. Consumer Electronics, 2011, 45(3): 598-603.

DOI: 10.1109/30.793546

Google Scholar

[7] Zuo Junyi, Pan Quan, Liang Yan. Adaptive background modeling method based on model switching [J]. Journal of automation, 2010, 33(5): 467-473.

Google Scholar

[8] Dai Kexue, Li Guohui, Tu Dan et al. Study on the current situation and prospect of background subtraction technology of video moving object detection [J]. Journal of Chinese image and graphics, 2012, 1(7): 919-927.

Google Scholar

[9] Wu Si, Lin Shouxun, Zhang Yongdong. Automatic segmentation of moving objects in video sequences based on dynamic background construction [J]. Journal of computer, 2011, 28(8): 1386-1392.

Google Scholar

[10] Chang Baobao. The maximum entropy model Natural Language Processing [D]. Computational Linguistics Institute of Peking University, 2010: 3-15.

Google Scholar

[11] Xiang Xiaowen. Chinese named entity recognition based on conditional random field [D]. Xiamen University, 2011: 2-14.

Google Scholar