Online Boosting tracking algorithm combined with occlusion sensing

被引：0
|
作者：
Wang Y.-W. [1 ]
Chen H.-C. [1 ]
Li S.-M. [1 ]
Gao C. [1 ]
机构：
[1] National Digital Switching System Engineering & Technology R&D Center, Zhengzhou
来源：
Tongxin Xuebao/Journal on Communications | 2016年 / 37卷 / 09期
基金：
中国国家自然科学基金;
关键词：
Object tracking; Occlusion sensing; Online Boosting; ORB feature;
D O I：
10.11959/j.issn.1000-436x.2016181
中图分类号：
学科分类号：
摘要：
Online Boosting tracking algorithm combined with occlusion sensing was presented. In this method, occlusion sensor was introduced to check the tracking results, and classifier updating strategy was adjusted depending on the occlusion checking results. By this way, the feature pool of the classifier can be kept pure, which will improve the tracking robustness under occlusion. Experimental results show that compared with traditional Boosting tracking algorithm, improved algorithm can solve the problem of occlusion very well. © 2016, Editorial Board of Journal on Communications. All right reserved.
引用
收藏
页码：92 / 101
页数：9
相关论文
共 16 条
  • [1] Luo H.L., Du F.F., Kong F.S., Pixel feature-weighted scale-adaptive object tracking algorithm, Journal on Communication, 36, 10, pp. 200-211, (2015)
  • [2] Zhang H.L., Hu S.Q., Yang G.S., Video object tracking based on appearance models learning, Journal of Computer Research and Development, 52, 1, pp. 177-190, (2015)
  • [3] Bai Y., Tang M., Robust tracking via weakly supervised ranking SVM, The IEEE Conference on Computer Vision and Pattern Recognition, pp. 1854-1861, (2012)
  • [4] Grabner H., Bischof H., On-line boosting and vision, The IEEE Conference on Computer Vision and Pattern Recognition, pp. 260-267, (2006)
  • [5] Cheng Y.L., Li B., Zhang W.C., Et al., An adaptive pedestrian tracking algorithm with prior knowledge, PR & AI, 22, 5, pp. 704-708, (2009)
  • [6] Yan J., Wu M.Y., On-line boosting based target tracking under occlusion, Optics and Precision Engineering, 20, 2, pp. 439-446, (2012)
  • [7] Adam A., Rivlin E., Shimshoni I., Robust fragments-based tracking using the integral histogram, The IEEE Conference on Computer Vision and Pattern Recognition, pp. 798-805, (2006)
  • [8] Hu H., Ma B., Wu Y., Et al., Kernel regression based online boosting tracking, Journal of Information Science and Engineering, 31, 1, pp. 267-282, (2015)
  • [9] Yang F., Lu H., Yang M.H., Robust visual tracking via multiple kernel boosting with affinity constraints, IEEE Transactions on Circuits and Systems for Video Technology, 24, 2, pp. 242-254, (2014)
  • [10] Grabner H., Leistner C., Bischof H., Semi-supervised on-line boosting for robust tracking, The European Conference on Computer Vision, pp. 234-247, (2008)
12