吉林大学学报(工学版) ›› 2017, Vol. 47 ›› Issue (1): 281-287.doi: 10.13229/j.cnki.jdxbgxb201701041

• Orginal Article • Previous Articles     Next Articles

Novelty fragments-based target tracking with multiple instance learning under occlusions

CAI Hua1, CHEN Guang-qiu1, LIU Guang-wen1, CHENG Shuai1, YU Hua-dong2   

  1. 1.School of Electronic and Information Engineering, Changchun University of Science and Technology, Changchun 130022,China;
    2.College of Mechanical and Electric Engineering, Changchun University of Science and Technology, Changchun 130022,China
  • Received:2016-02-21 Online:2017-01-20 Published:2017-01-20

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Abstract: To solve the problem that tracking algorithm may lead to drift or failure due to the accumulated error under the occlusion environment, a Multiple instance learning based Fragment Tracker (MFT) is proposed. In this MFT, the random ferns is used as the basic detector. To improve the adaption of the target change and the precision of the learning, the multiple instance learning is introduced to online update the detector. The object is segmented into fragments and parts of them are selected as the candidate set. The candidate block is tracked by the corresponding detector. The object can be finally located by the selected blocks. A real-time valid detection is made for the candidate blocks and the invalid block is replaced with an appropriate block to improve the robustness of the tracking. Experiments on variant challenging image sequence in the occlusion environment were carried out. Results show that, compared with the state-of-art trackers, the proposed MFT solves the problem of target drift and failure efficiently and has higher accuracy and better robust.

Key words: information processing, random ferns detector, multiple instance learning, fragment, invalid block replacement

CLC Number: 

  • TP391.41
[1] 薛陈, 朱明, 刘春香. 遮挡情况下目标跟踪算法综述[J].中国光学与应用光学, 2009, 2(5):388-394.
Xue Chen, Zhu Ming, Liu Chun-xiang. Review of tracking algorithms under occlusions[J]. Chinese Journal of Optics and Applied Optics, 2009, 2(5): 388-394.
[2] 颜佳, 吴敏渊. 遮挡环境下采用在线Boosting的目标跟踪[J]. 光学精密工程, 2012,20(2):439-446.
Yan Jia, Wu Min-yuan. On-line boosting based target tracking under occlusion[J]. Optics and Precision Engineering, 2012,20(2):439-446.
[3] Alper Y, Omar J, Mubarak S. Object tracking: a survey[J]. ACM Computing Surveys, 2006, 38(4):112-123.
[4] Medeiros H, Park J, Kak A. A parallel color-based particle filter for object tracking[C]∥IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Anchorage, USA, 2008: 1-8.
[5] Zhang T Z, Ghanem B, Xu C, et al. Object tracking by occlusion detection via structured sparse learning[C]∥IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Portland, USA,2013: 1033-1040.
[6] Zhang S P,Wu J P, Tian Y, et al. Robust visual tracking based on occlusion detection and particle redistribution[C]∥The Second International Conference on Internet Multimedia Computing and Service, New York, USA, 2010: 159-162.
[7] Mei X, Ling H B, Wu Y, et al. Minimum error bounded efficient L1 tracker with occlusion detection[C]∥IEEE Computer Society Conference on Computer Vision and Pattern Recognition, New York, USA, 2011: 1257-1264.
[8] 陈东成, 朱明, 高文, 等. 在线加权多示例学习实时目标跟踪[J]. 光学精密工程, 2014, 22(6): 1661-1667.
Chen Dong-cheng, Zhu Ming, Gao Wen, et al. Real-time object tracking via online weighted multiple instance learning[J]. Optics and Precision Engineering, 2014, 22(6): 1661-1667.
[9] Andriluka M, Roth S, Schiele B. People-tracking-by-detection and people-detection-by-tracking[C]∥IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Anchorage,USA, 2008: 1-8.
[10] Gall J, Yao A, Razavi N, et al.. Hough forests for object detection, tracking, and action recognition[J]. IEEE Trans Pattern Analysis and Machine Intelligence, 2011, 33(11): 2188-2202.
[11] Kalal Z,Matas J,Mikolajczyk K.Online learning of robust object detectors during unstable tracking[C]∥IEEE International Conference on Computer Vision Workshops, Kyoto, Japan, 2009: 1417-1424.
[12] 贾松敏, 文林风, 王丽佳. 基于多模板回归加权均值漂移的人体目标跟踪[J]. 光学精密工程, 2016, 24(9): 2339-2346.
Jia Song-min, Wen Lin-feng, Wang Li-jia. Person tracking based on multi-template regression weighted mean shift[J]. Optics and Precision Engineering, 2016, 24(9): 2339-2346.
[13] Adam A, Rivlin E, Shimshoni I. Robust fragment-based tracking using the integral histogram[C]∥IEEE Computer Society Conference on Computer Vision and Pattern Recognition, New York,USA,2006: 798-805.
[14] Naik N, Patil S, Joshi M. A fragment based scale adaptive tracker with partial occlusion handling[C]∥IEEE Region 10 Conference, Singapore, 2009: 1-6.
[15] Kalal Z, Mikolajczyk K, Matas J. Tracking-learning-detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(7): 1409-1422.
[16] Babenko B, Yang M H, Belongle S. Robust object tracking with online multiple instance learning[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011, 33(8): 1619-1632.
[17] Zhang K, Zhang L, Yang M H. Fast compressive tracking[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014, 36(10): 2002-2015.
[18] Dinh T B, Vo N, Medion G. Context tracker: exploring supporters and distracters in unconstrained environments[C]∥IEEE Conference on Computer Vision and Pattern Recognition,Providence,USA,2011: 1177-1184.
[19] Ross D A, Lim J, Lin R S, et al. Incremental learning for robust visual tracking[J]. International Journal of Computer Vision, 2008, 77(1-3):125-141.
[20] Grabner H, Grabner M, Bischof H. Real-time tracking via on-line boosting[C]∥British Machine Vision Conference, Edinburgh, Britain, 2006:47-56.
[21] 罗艳,项俊, 严明君,等. 基于多示例学习和随机蕨丛检测的在线目标跟踪[J]. 电子与信息学报,2014,36(7):1605-1611.
Luo Yan, Xiang Jun, Yan Ming-jun, et al. Online target tracking based on mulitiple instance learning and random ferns detection[J]. Journal of Electronics & Information Technology, 2014,36(7):1605-1611.
[22] Wu Y, Lim J, Yang M H. Online object tracking:a benchmark[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 36(4): 2411-2418.
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