Object tracking based on Camshift with multi-feature fusion

被引：14
作者：
机构：
[1] College of Information, Zhejiang Sci-Tech University, Hangzhou
[2] Business Administration College, Zhejiang University of Finance and Economics, Hangzhou
[3] College of Mechanical Engineering and Automation, Zhejiang Sci-Tech University, Hangzhou
来源：
Zhou, Z. (zhouzhiyu1993@163.com) | 1600年 / Academy Publisher卷 / 09期
关键词：
Camshift; GM(1; 1); Object tracking; SIFT features;
D O I：
10.4304/jsw.9.1.147-153
中图分类号：
学科分类号：
摘要：
It is very hard for traditional Camshift to survive of drastic interferences and occlusions of similar objects. This paper puts forward an innovative tracking method using Camshift with multi-feature fusion. Firstly, SIFT features and edge features of the Camshift in RGB space are counted to reduce the probability of disruption by occlusion and clutter. Then, the texture features are collected to resolve the problems of analogue interference, the texture similarity between current frame and previous frames are calculated to determine the object area. The paper also describes the GM(1,1) prediction model, which could solve the occlusion problems in a novel way. Finally, through the motion trajectory, it can anticipate the exact position of the object. The results of several tracking tasks prove that our method has solved problems of occlusions, interferences and shadows. And it performs well in both tracking robustness and computational efficiency. © 2014 ACADEMY PUBLISHER.
引用
收藏
页码：147 / 153
页数：6
相关论文
共 15 条
  • [1] Dorin C., Visvanathan R., Peter M., Real-time tracking of non-rigid objects using Mean Shift, Proc of the IEEE Conf On Computer Vision and Pattern Recognition, pp. 142-149, (2000)
  • [2] Dorin C., Visvanathan R., Peter M., Kernel-based object tracking, IEEE Trans. On Pattern Analysis and Machine Intelligence, 25, 5, pp. 564-577, (2003)
  • [3] Bradski Gray R., Computer Vision Face Tracking For Use in a Perceptual User Interface, Intel Technology Journal, 2, 2, pp. 1-15, (1998)
  • [4] Xuanjing S., Bo Z., CamShift tracker based on image moments, Journal of Beijing University of Technology, 38, 1, pp. 105-109, (2012)
  • [5] Li C., Tiegen L., Hongli L., Et al., Face tracking based on Haar detection and improved Camshift algorithm, Journal of Optoelectronics Laser, 22, 12, pp. 1852-1856, (2011)
  • [6] Hongguang S., Jin Z., Yantao L., Et al., Optimized Particle Filter Tracking by CamShift Based on Multi-feature, Opto-Electronic Engineering, 37, 2, pp. 1-6, (2010)
  • [7] Zhaowen W., Xiaokang Y., Yi X., Et al., CamShift guided particle filter for visual tracking, Pattern Recognition Letters, 30, 4, pp. 407-413, (2009)
  • [8] Xin W., Zhenmin T., An improved camshift-based particle filter algorithm for real-time target tracking, Journal of Image and Graphics, V15, 15, 10, pp. 1507-1515, (2010)
  • [9] Li J., Ji Z., Zhenhuan Z., Et al., Object tracking using improved Camshift with SURF method, 2011 IEEE International Workshop on Open-Source Software For Scientific Computation, pp. 136-141, (2011)
  • [10] David E., Erich B., Danielet K., Et al., Fast and robust CAMShift tracking, 2010 IEEE Computer Society Conference On Computer Vision and Pattern Recognition, pp. 9-16, (2010)
12