Probabilistic model single target tracking framework for video surveillance system

LI Jing-yu; LIU Yan-ying; TIAN Rui; WANG Yan-jie; JIANG Rui-kai

doi:10.3788/OPE.20152307.2093

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Probabilistic model single target tracking framework for video surveillance system

更新时间：2020-08-12

- Probabilistic model single target tracking framework for video surveillance system
- Optics and Precision Engineering Vol. 23, Issue 7, Pages: 2093-2099(2015)
- 作者机构：
  
  中国科学院长春光学精密机械与物理研究所,吉林长春,中国,130033
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20152307.2093
  CLC： TN941.1
- Received：15 December 2014，
  
  Revised：10 February 2015，
  
  Published：25 July 2015
- 稿件说明：
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李静宇, 刘艳滢, 田睿等. 视频监控系统中的概率模型单目标跟踪框架[J]. 光学精密工程, 2015,23(7): 2093-2099

LI Jing-yu, LIU Yan-ying, TIAN Rui etc. Probabilistic model single target tracking framework for video surveillance system[J]. Editorial Office of Optics and Precision Engineering, 2015,23(7): 2093-2099
李静宇, 刘艳滢, 田睿等. 视频监控系统中的概率模型单目标跟踪框架[J]. 光学精密工程, 2015,23(7): 2093-2099 DOI： 10.3788/OPE.20152307.2093.

LI Jing-yu, LIU Yan-ying, TIAN Rui etc. Probabilistic model single target tracking framework for video surveillance system[J]. Editorial Office of Optics and Precision Engineering, 2015,23(7): 2093-2099 DOI： 10.3788/OPE.20152307.2093.

摘要

针对视频监控的特点与跟踪目标的强机动性

提出了一种新的基于概率模型的目标跟踪框架

从目标表观模型、系统动态模型以及系统观测模型3个方面对当前标准的粒子滤波目标跟踪方法进行了改进。首先

考虑人眼细胞的分布特点

基于人眼分布结构建立目标表观模型来提高跟踪系统抵抗局部遮挡的能力;然后

建立基于自适应目标运动的系统动态模型

提高跟踪算法对快速机动目标的鲁棒性;最后

采用实时更新的系统观测模型

有效避免目标在遇到遮挡、光照变化、剧烈变形等情况下发生的跟踪漂移现象。实验结果表明

本文算法的正确跟踪率可达98%;平均跟踪误差小于6个像元。实验证明本文算法在保证系统跟踪精度要求的同时

具有计算量小、抗干扰能力强等特点。

Abstract

According to characteristics of video surveillance and strong mobility of tracking targets

a novel target tracking framework based on a probability model was proposed. The current standard particle filtering target algorithm was improved based on a target appearance model

a systemic dynamic model

and a systemic observation model. Firstly

the target appearance model was established by taking the distribution of human eye cell into account to improve its resistance capability for partial occlusion of local occlusion. Then

the systemic dynamic model based on the adaptive target movement was built to improve the robustness of tracking framework for the fast moving target. Finally

the systemic observation model with online update was established to prevent the tracking shift when the target faced the occlusion

illumination changes

severe deformation

etc.

effectively. Experimental results show that the proposed algorithm achieves 98% of correct tracking rate

and the average tracking error is less than 6 pixels. The proposed method satisfies the video surveillance system require ments for stabilization

reliability

higher precision

less computing cost

as well as strong anti-jamming.

关键词

Keywords

references

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赵鹏,沈庭芝,单宝堂. 一种基于粒子滤波的无人机电视导引系统目标跟踪算法 [J]. 光学精密工程,2008, 16(1):134-140. ZHAO P, SHEN T ZH, SHAN B T. An object tracking algorithm for TV guiding system of UAV based on particle filter [J]. Opt. Precision Eng., 2008, 16(1):134-140. (in Chinese)

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杜超,刘伟宁,刘恋. 一种基于卡尔曼滤波及粒子滤波的目标跟踪算法 [J]. 液晶与显示, 2011, 26(3): 384-389. DU C, LIU W N, LIU L. Target tracking algorithm based on Kalman filter and particle filter [J]. Chinese Journal of Liquid Crystals and Displays, 2011, 26(3): 384-389. (in Chinese)

宋策, 张葆,尹传历. 适于机载环境对地目标跟踪的粒子滤波设计 [J]. 光学精密工程,2014, 22(4):1037-1047. SONG C, ZHANG B, YIN CH L. Particle filter design for tracking ground targets in airborne environment [J]. Opt. Precision Eng., 2014, 22(4):1037-1047. (in Chinese)

ISARD M,BLAKE A. Condensation-Conditional density propagation for visual tracking [J]. Int. J. Comput. Vis.. 1998, 29(1): 5-28.

王书朋,姬红兵. 用于目标跟踪的自适应粒子滤波算法[J]. 系统仿真学报, 2010, 22(3): 630-633. WANG SH P, JI H B. Adaptive particle filtering for efficient object tracking [J]. Journal of System Simulation, 2010, 22(3): 630-633. (in Chiniese)

ROSS D, LIM J, LIN R, et al.. Incremental learning for robust visual tracking [J]. Internat. J. Comput. Vision, 2008, 77 (3): 125-141.

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