采用改进辅助粒子滤波的红外多目标跟踪

龚俊亮; 何昕; 魏仲慧; 郭敬明

doi:10.3788/OPE.20122002.0413

您当前的位置：

首页 >

文章列表页 >

采用改进辅助粒子滤波的红外多目标跟踪

信息科学 | 更新时间：2020-08-12

- 采用改进辅助粒子滤波的红外多目标跟踪
- Multiple infrared target tracking using improved auxiliary particle filter
- 光学精密工程 2012年20卷第2期页码：413-421
- 作者机构：
  
  1. 中国科学院长春光学精密机械及物理研究所,吉林长春,130033
  2. 中国科学院研究生院北京,100039
- 作者简介：
- 基金信息：
  
  国家自然科学基金资助项目(No.60902067)()
- DOI：10.3788/OPE.20122002.0413
  中图分类号： TN219;TP391
- 收稿日期：2011-06-02，
  
  修回日期：2011-06-22，
  
  网络出版日期：2012-02-25，
  
  纸质出版日期：2012-02-25
- 稿件说明：
移动端阅览
龚俊亮, 何昕, 魏仲慧, 郭敬明. 采用改进辅助粒子滤波的红外多目标跟踪[J]. 光学精密工程, 2012,20(2): 413-421

GONG Jun-liang, HE Xin, WEI Zhong-hui, GUO Jing-ming. Multiple infrared target tracking using improved auxiliary particle filter[J]. Editorial Office of Optics and Precision Engineering, 2012,20(2): 413-421
龚俊亮, 何昕, 魏仲慧, 郭敬明. 采用改进辅助粒子滤波的红外多目标跟踪[J]. 光学精密工程, 2012,20(2): 413-421 DOI： 10.3788/OPE.20122002.0413.

GONG Jun-liang, HE Xin, WEI Zhong-hui, GUO Jing-ming. Multiple infrared target tracking using improved auxiliary particle filter[J]. Editorial Office of Optics and Precision Engineering, 2012,20(2): 413-421 DOI： 10.3788/OPE.20122002.0413.

摘要

结合改进的辅助粒子滤波与马尔科夫随机场

提出一种多目标跟踪算法来跟踪红外场景中的多个目标。依据目标区域的灰度直方图描述目标

使用标准辅助粒子滤波对各目标的采样粒子集进行粗略优化

同时在辅助粒子采样过程中引入Mean-shift算法来提高粒子采样效率

解决多目标跟踪时粒子数量呈指数级增长的问题

并进一步提高算法的实时性。针对多目标跟踪常出现的目标遮挡导致跟踪失败的问题

引入图模型理论

利用马尔科夫随机场来表示多目标跟踪模型

将多目标的跟踪问题转换为图模型的推理问题。实验结果表明

该跟踪算法使用较少粒子便能实现跟踪

跟踪正确率达84%

且能有效解决多目标跟踪时的相互遮挡问题。

Abstract

An algorithm combining an improved Auxiliary Particle Filter(APF) with a Markov random field is proposed to achieve multiple target tracking in an infrared scene. First

targets are described according to the gray histogram of target regions.Then

sampling particles of all targets are optimized roughly by using standard APF. Meanwhile

Mean-shift is introduced to the process of auxiliary particle sampling to improve the exponential growth of particle numbers and to increase the percentage of efficient particles and the real-time ability. As for the failure tracking from that targets often are covered each other

a graphic model theory is introduced

in which multi-tracking model by the Markov random field is used to describe the multi-tracking model and convert the problem of multi-target tracking into an inferential problem of the graph model. Results indicate that the new algorithm proposed can track targets only by a few particles

and the accurate rate for multi-target tracking is up to 84%

the failure tracking caused by targets covered mutually can be solved effectively.

关键词

Keywords

references

田隽,钱建生,李世银,等. 应用自适应多测量融合粒子滤波的视场跟踪[J]. 光学精密工程,2010,18(10):2254-2261. TIAN J, QIAN J SH, LI SH Y, et al.. Visual tracking with adaptive multi-cue fusion particle filter[J]. Opt. Precision Eng., 2010,18(10):2254-2261. (in Chinese)[2] 周斌,王军政,沈伟. 基于组合带宽均值迁移的快速目标跟踪[J]. 光学精密工程,2010,18(10):2297-2305. ZHOU B, WANG J ZH, SHEN W. Fast object tracking with multi-bandwidth Mean Shift[J]. Opt. Precision Eng., 2010,18(10):2297-2305. (in Chinese)[3] 李甫,石光明,张犁,等. 一种用于光电目标跟踪的变窗宽核粒子滤波[J]. 光学精密工程,2010,18(3):716-722. LI F, SHI G M, ZHANG L, et al.. Variable bandwidth kernel particle filtering used for optoelectronic target tracking[J]. Opt. Precision Eng., 2010,18(3):716-722. (in Chinese)[4] 王国良,刘金国. 基于粒子滤波的多自由度运动目标跟踪[J]. 光学精密工程,2011,19(4):864-869. WANG G L, LIU J G. Moving object tracking with multi-degree-of-freedom based on particle filters[J]. Opt. Precision Eng., 2011,19(4):864-869.(in Chinese)[5] GORDON N, SALMOND D, SMITH A. Novel approach to nonlinear/non-gaussian bayesian state estimation[J]. IEE Proceedings on Radar and Signal Processing, 1993,140(2):107-113.[6] TING Y, YING W. Collaborative tracking of multiple targets . Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Washington, DC: IEEE Press,1995:1-8. [7] CHANG C, ANSARA R. Kernel particle filter for visual tracking [J]. IEEE Signal Processing Letters,2005,12(3):242-245.[8] SHAN C F, TAN T N, WEI Y CH. Real-time hand tracking using a mean-shift embedded particle filter [J]. Pattern Recognition,2007,40:1958-1962. [9] PATRICK P, VERMAAK J. Data fusion for visual tracking with particle [J]. IEEE, 2004,92(3):495-513.[10] 蒋永馨,金俣欣,王孝通,等. 基于马尔可夫随机场的运动目标检测[J]. 计算机工程与应用,2010,46(15):182-184. JIANG Y X, JIN Y X, WANG X T, et al.. Moving object detection based on Markov Random Field model [J]. Computer Engineering and Applications, 2010,46(15):182-184. (in Chinese)[11] PARAGIOS N, RSMESH V. A MRF-based approach for real-time subway monitoring . Processing of Computer Vision and Pattern Recognition, 2001:295-313.[12] 薛陈,朱明,陈爱华. 一种鲁棒的基于改进Mean-shift的目标跟踪算法[J]. 光学精密工程,2010,18(1):234-239. XUE CH, ZHU M, CHEN A H. Robust object tracking based on improved Mean-shift algorithm [J]. Opt. Precision Eng., 2010,18(1):234-239. (in Chinese)

浏览量

456

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

支撑点扩展快速立体匹配方法的设计与应用