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

被引：0
|
作者：
Cai H. [1 ]
Chen G.-Q. [1 ]
Liu G.-W. [1 ]
Cheng S. [1 ]
Yu H.-D. [2 ]
机构：
[1] School of Electronic and Information Engineering, Changchun University of Science and Technology, Changchun
[2] College of Mechanical and Electric Engineering, Changchun University of Science and Technology, Changchun
来源：
Chen, Guang-Qiu (guangqiu_chen@126.com) | 1600年 / Editorial Board of Jilin University卷 / 47期
关键词：
Fragment; Information processing; Invalid block replacement; Multiple instance learning; Random ferns detector;
D O I：
10.13229/j.cnki.jdxbgxb201701041
中图分类号：
学科分类号：
摘要：
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. © 2017, Editorial Board of Jilin University. All right reserved.
引用
收藏
页码：281 / 287
页数：6
相关论文
共 22 条
  • [1] Xue C., Zhu M., Liu C.-X., Review of tracking algorithms under occlusions, Chinese Journal of Optics and Applied Optics, 2, 5, pp. 388-394, (2009)
  • [2] Yan J., Wu M.-Y., On-line boosting based target tracking under occlusion, Optics and Precision Engineering, 20, 2, pp. 439-446, (2012)
  • [3] Alper Y., Omar J., Mubarak S., Object tracking: a survey, ACM Computing Surveys, 38, 4, pp. 112-123, (2006)
  • [4] Medeiros H., Park J., Kak A., A parallel color-based particle filter for object tracking, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1-8, (2008)
  • [5] Zhang T.Z., Ghanem B., Xu C., Et al., Object tracking by occlusion detection via structured sparse learning, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1033-1040, (2013)
  • [6] Zhang S.P., Wu J.P., Tian Y., Et al., Robust visual tracking based on occlusion detection and particle redistribution, The Second International Conference on Internet Multimedia Computing and Service, pp. 159-162, (2010)
  • [7] Mei X., Ling H.B., Wu Y., Et al., Minimum error bounded efficient L1 tracker with occlusion detection, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1257-1264, (2011)
  • [8] Chen D.-C., Zhu M., Gao W., Et al., Real-time object tracking via online weighted multiple instance learning, Optics and Precision Engineering, 22, 6, pp. 1661-1667, (2014)
  • [9] Andriluka M., Roth S., Schiele B., People-tracking-by-detection and people-detection-by-tracking, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1-8, (2008)
  • [10] Gall J., Yao A., Razavi N., Et al., Hough forests for object detection, tracking, and action recognition, IEEE Trans Pattern Analysis and Machine Intelligence, 33, 11, pp. 2188-2202, (2011)
123