[1]王燕妮,王建平.约束条件下的优选进化目标跟踪算法分析[J].西安科技大学学报,2017,(04):559-564.[doi:10.13800/j.cnki.xakjdxxb.2017.0416]
 WANG Yan-ni,WANG Jian-ping.Analysis of target tracking algorithm of preferred evolutionary under constraints[J].Journal of Xi'an University of Science and Technology,2017,(04):559-564.[doi:10.13800/j.cnki.xakjdxxb.2017.0416]
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约束条件下的优选进化目标跟踪算法分析(/HTML)
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西安科技大学学报[ISSN:1672-9315/CN:61-1434/N]

卷:
期数:
2017年04期
页码:
559-564
栏目:
出版日期:
2017-07-30

文章信息/Info

Title:
Analysis of target tracking algorithm of preferred evolutionary under constraints
文章编号:
1672-9315(2017)04-0559-06
作者:
王燕妮1王建平2
1.西安建筑科技大学 信息与控制工程学院,陕西 西安 710055; 2.北京航空航天大学 软件学院,北京 100191
Author(s):
WANG Yan-ni1WANG Jian-ping2
1.School of Information and Control Engineering,Xi'an University of Architecture and Technology,Xi'an 710055,China; 2.School of Software,Beihang University,Beijing 100191,China
关键词:
视频监控 目标跟踪 遗传算法 粒子滤波
Keywords:
Key words:video surveillance target tracking genetic algorithm particle filter
分类号:
TP 317.4
DOI:
10.13800/j.cnki.xakjdxxb.2017.0416
文献标志码:
A
摘要:
针对传统粒子滤波算法的退化和容易受到背景干扰等问题,结合遗传算法思想,提出一种约束条件下的优选进化粒子滤波的视频目标跟踪算法。首先采用约束条件检测目标,在参考帧生成初始父代粒子种群。利用遗传算法的特点,设置优选进化策略,选择优秀粒子遗传到子代。其次通过制定合适的交叉及变异规则,对未选中的粒子进行相应的运算直到迭代次数。最后通过每个粒子的权值计算其状态,获得运动目标轨迹。仿真实验结果表明,该方法不受背景变化等影响,不仅实现简单,而且有较稳定的跟踪效果。
Abstract:
Abstract:In order to solve the problem of the degradation of traditional particle filter algorithm and the interference of background,a new target tracking algorithm of preferred genetic evolutionary is proposed.Firstly,according to the constraints,the moving targets are detected and initial parent particle population are generated in the reference frame.The excellent particles are selected to be offspring based on optimal evolution strategies.Then,making appropriate crossover and mutation rules,the unselected particles are not selected to perform the operation until the number of iterations.Finally,the target trajectory is obtained by calculating object states of each particle.Simulation results show that the new algorithm is not affected by the background changes.It is not only simple,but also more stable for tracking effect than the existing algorithm.

参考文献/References:

[1] Malik Morshidi,Tardi Tjahjadi.Gravity optimised particle filter for hand tracking[J].Pattern Recognition,2014,47(1):194-207.
[2] Numm iaro K,M erier E K,GoolL V.An adaptive color based particle filter[J].Image and Vision Computing,2003,21(1):99-110.
[3] Jungho Kim,Zhe Lin,In So Kweon.Rao-blackwellized particle filtering with Gaussian mixture models for robust visual tracking[J].Computer Vision and Image Understanding,2014,125(8):128-137.
[4] 张 菁,沈兰荪,高静静.基于视觉注意机制的感兴趣区检测[J].光子学报,2009,38(6):1 561-1 565. ZHANG Jing,SHEN Lan-sun,GAO Jing-jing.Regions of interest detection based on visual attention mechanism[J].Acta Photonica Sinica,2009,38(6):1 561-1 565.
[5]Huang C B,Liu Q,Yu S S.Region of interest extraction from color image based on visual saliency[J].The Journal of Supercomputing,2011,58(1):20-33.
[6]ZUO Jun-yi,LIANG Yan,ZHANG Yi-zhe,et al.Particle filter with multimode sampling strategy[J].Signal Processing,2013,93(11):3 192-3 201.
[7]Gronat P,Sivic J,Obozinski G,et al.Learning and calibrating per-location classifiers for visual place recognition[J].International Journal of Computer Vision,2016,118(3):319-336.
[8]Li H,Zhang Y,Wang J,et al.Inequality-con-strained RPCA for shadow removal and foreground detection[J].IEICE Transaction on Information and Systems,2015,98(6):1 256-1 259.
[9]陈善静,杨 华,曾 凯,等.基于遗传算法的粒子滤波跟踪算法[J].光电工程,2010,37(10):16-20. CHEN Shan-jing,YANG Hua,ZENG Kai,et al.Particle filter tracking algorithm based on genetic algorithm[J].Opto-Electronic Engineering,2010,37(10):16-20.
[10]Dang C,Radha H.RPCA-KFE:key frame extraction for video using robust principal component analysis[J].IEEE Transactions on Image Processing,2015,24(11):3 742-3 753.
[11]Fevrier Valdez,Patricia Melin,Oscar Castillo.Modular neural networks architecture optimization with a new nature inspired method using a fuzzy combination of particle swarm optimization and genetic algorithms[J].Information Sciences,2014,270(6):143-153.
[12]赵小川.现代数字图像处理技术提高及应用案例详解[M].北京:北京航空航天大学出版社,2012. ZHAO Xiao-chuan.Improvement and application case of modern digital image processing technology[M].Beijing:Beihang University Press,2012.
[13]Bouwnans T, Zahzah E H.Robust PCA via principal component pursuit:A review for a comparative evaluation in video surveillance[J].Computer Vision and Image Understanding,2014,122(4):22-34.
[14]Luan X,Fang B,Liu L H,et al.Extracting sparse error of robust PCA for face recognition in the presence of varying illumination and occlusion[J].Pattern Recognition,2014,47(2):495-508.

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备注/Memo

备注/Memo:
基金项目:陕西省自然科学基础研究计划(2016JM6079); 陕西省社会发展科技攻关项目(2013K13-04-08); 陕西省教育厅专项科研项目(14JK1429) 通讯作者:王燕妮(1975-),女,陕西蒲城人,博士,副教授,E-mail:wangyn02@126.com
更新日期/Last Update: 1900-01-01