Visual tracking using bat-inspired algorithm

被引：0
|
作者：
Gao M.-L. [1 ]
Yin L.-J. [1 ]
Jiang J. [2 ]
Shen J. [1 ]
机构：
[1] College of Electrical and Electronic Engineering, Shandong University of Technology, Zibo, 255000, Shandong
[2] School of Computer Science, Southwest Petroleum University, Chengdu
来源：
| 1600年 / Beijing University of Posts and Telecommunications卷 / 39期
关键词：
Bat algorithm; Iterative termination condition; Parameters determination; Visual tracking;
D O I：
10.13190/j.jbupt.2016.05.015
中图分类号：
学科分类号：
摘要：
A new visual tracking method based on bat algorithm (BA) was presented, in which, the BA-based tracking architecture is proposed. Then, the impact of the iterative termination condition, population size, pulse frequency increasing coefficient and pulse amplitude attenuation coefficient on the tracking performance are studied and these parameters are determined. To demonstrate the tracking ability of the proposed tracker, comparative studies of tracking accuracy and speed of the BA-based tracker with three representative trackers, namely, particle filter, meanshift, particle swarm optimization are given. Comparative results show that the proposed algorithm outperforms the other three trackers. © 2016, Editorial Department of Journal of Beijing University of Posts and Telecommunications. All right reserved.
引用
收藏
页码：72 / 77
页数：5
相关论文
共 10 条
  • [1] Wu Y., Lim J., Yang M., Object tracking benchmark, IEEE T Pattern Anal, 37, 9, pp. 1834-1848, (2015)
  • [2] Hou Z., Han C., A survey on visual tracking, Acta Automatica Sinica, 32, 2, pp. 603-617, (2006)
  • [3] Li X., Hu W., Shen C., Et al., A survey of appearance models in visual object tracking, ACM T Intel Syst Tec, 4, 4, (2013)
  • [4] Gao M., He X., Luo D., Et al., Object tracking based on harmony search: comparative study, J Electron Imaging, 1, 4, (2012)
  • [5] Comaniciu D., Ramesh V., Meer P., Kernel-based object tracking, IEEE T Pattern Anal, 25, 5, pp. 564-577, (2003)
  • [6] Isard M., Blake A., Condensation-conditional density propagation for visual tracking, INT J Comput Vision, 29, 1, pp. 5-28, (1998)
  • [7] Zhang X., Hu W., Maybank S., Et al., Sequential particle swarm optimization for visual tracking, IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Anchorage, pp. 1-8, (2008)
  • [8] Park S., Hwang J.P., Kim E., Et al., A new evolutionary particle filter for the prevention of sample impoverishment, IEEE T Evolut Comput, 13, 4, pp. 801-809, (2009)
  • [9] Minami M., Agbanhan J., Asakura T., Manipulator visual servoing and tracking of fish using a genetic algorithm, Industrial Robot-an International Journal, 26, 4, pp. 278-289, (1999)
  • [10] Yang X., A new metaheuristic bat-inspired algorithm, Nature Inspired Cooperative Strategies for Optimization, 284, pp. 65-74, (2010)
1