[1]刘韶涛,姚灿荣.结合PSO的改进压缩跟踪方法[J].华侨大学学报(自然科学版),2017,38(1):121-126.[doi:10.11830/ISSN.1000-5013.201701024]
 LIU Shaotao,YAO Canrong.Improved Compress Tracking Algorithm Based on PSO[J].Journal of Huaqiao University(Natural Science),2017,38(1):121-126.[doi:10.11830/ISSN.1000-5013.201701024]
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结合PSO的改进压缩跟踪方法()
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《华侨大学学报(自然科学版)》[ISSN:1000-5013/CN:35-1079/N]

卷:
第38卷
期数:
2017年第1期
页码:
121-126
栏目:
出版日期:
2017-01-09

文章信息/Info

Title:
Improved Compress Tracking Algorithm Based on PSO
文章编号:
1000-5013(2017)01-0121-06
作者:
刘韶涛 姚灿荣
华侨大学 计算机科学与技术学院, 福建 厦门 361021
Author(s):
LIU Shaotao YAO Canrong
College of Computer Science and Technology, Huaqiao University, Xiamen 361021, China
关键词:
目标跟踪 压缩感知 粒子群优化 多尺度
Keywords:
visual tracking compress sensing particle swarm optimization multi-scale
分类号:
TP391.3
DOI:
10.11830/ISSN.1000-5013.201701024
文献标志码:
A
摘要:
针对基于在线检测的跟踪方法中目标在多尺度空间中的搜索和匹配问题,结合粒子群优化算法(PSO)和压缩感知思想,提出一种鲁棒的多尺度目标跟踪算法.首先,通过粒子群在多尺度空间中采集样本;然后,经过压缩感知提取特征;最后,通过粒子的迭代计算,搜索出当前目标的最佳匹配位置.实验结果表明:提出的算法能较好地适应目标的多尺度变化,在快速性和鲁棒性上具有更好的性能.
Abstract:
For the searching and matching problem in multi-scale space of online detecting tracking method, a robust multi-scale tracking algorithm was proposed based on particle swarm optimization(PSO)and compress sensing. Firstly, feature was sampled with particles in multi-scale space. Then feature was extracted by compress sensing. Finally, targets would be searched quickly and robustly after calculate the best fitness and position of all the particle. The experimental results demonstrate that the proposed algorithm can adapt target in multi-scale change and has a better performance in robustness and rapidity.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期: 2015-06-02
通信作者: 刘韶涛(1969-),男,副教授,主要从事软件体系结构的研究.E-mail:shaotaol@hqu.edu.cn.
基金项目: 国务院侨办科研基金资助项目(09QZR02)
更新日期/Last Update: 2017-01-20