[1]陈真,王钊.元认知粒子滤波目标跟踪算法[J].智能系统学报,2015,10(03):387-392.[doi:10.3969/j.issn.1673-4785.201405052]
 CHEN Zhen,WANG Zhao.Object tracking algorithm with metacognitive model-based particle filters[J].CAAI Transactions on Intelligent Systems,2015,10(03):387-392.[doi:10.3969/j.issn.1673-4785.201405052]
点击复制

元认知粒子滤波目标跟踪算法(/HTML)
分享到:

《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第10卷
期数:
2015年03期
页码:
387-392
栏目:
出版日期:
2015-06-25

文章信息/Info

Title:
Object tracking algorithm with metacognitive model-based particle filters
作者:
陈真 王钊
中国石油大学(华东) 信息与控制工程学院, 山东 青岛 266580
Author(s):
CHEN Zhen WANG Zhao
College of Information and Control Engineering, China University of Petroleum, Qingdao 266580, China
关键词:
目标跟踪粒子滤波元认知元认知模型元认知粒子滤波
Keywords:
object trackingparticle filtermetacognitionmetacognitive modelmetacognitive particle filter (MCPF)
分类号:
TP391
DOI:
10.3969/j.issn.1673-4785.201405052
文献标志码:
A
摘要:
经典粒子滤波算法进行复杂背景下的目标跟踪时,不能及时将突变的背景信息更新到目标模板中,容易造成跟踪不稳定,尤其是当突变背景与目标模板相似时,容易造成目标丢失.为了解决此类问题,提出了具有元认知能力的粒子滤波(MCPF)目标跟踪算法.首先,MCPF对初始化目标模板进行认知,建立目标认知模板和背景认知模板作为MCPF的元认知知识成分,然后,MCPF监控多次迭代后采样粒子的观测信息,刺激产生MCPF元认知体验成分,从而有效地调节MCPF算法的决策机制.实验结果表明,该算法跟踪实时性高、稳定、可靠,当背景发生突变时,能够及时监控到突变,并快速调节决策机制,实现稳定的目标跟踪.
Abstract:
Traditional particle filter object tracking in complex backgrounds cannot update the sudden backgrounds information to the target template timely. Therefore, it is easy to cause tracking instability, even lose object when the sudden background is similar to the target template. An object tracking algorithm with metacognitive particle filters (MCPF) is proposed to solve this problem. Firstly, MCPF establishes metacognitive knowledge composed of object cognitive models and background cognitive model. Next, MCPF monitors the observation information of the sampling particles after multiple iterations, simulates the observation information to generate metacognitive experience and realizes object-tracking stability. Extensive experimental results showed that the proposed algorithm improves the instantaneity, stability and reliability of traditional PF object tracking algorithm. When the background suddenly changes, it can timely monitor it and quickly adjust decision-making mechanism to realize stable target tracking.

参考文献/References:

[1] GORDON N J, SALMOND D J, SMITH A F M. Novel approach to nonlinear/non-Gaussian Bayesian state estimation[J]. IEE Proceedings F Radar and Signal Processing, 1993, 140(2): 107-113.
[2] ARULAMPALAM M S, MASKELL S, GORDON N, et al. A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking[J]. IEEE Transactions on Signal Processing, 2002, 50(2): 174-188.
[3] ISARD M, BLAKE A. Condensation-conditional density propagation for visual tracking[J]. International Journal on Computer Vision, 1998, 29(1): 5-28.
[4] MEI Xue, LING Haibin. Robust visual tracking and vehicle classification via sparse representation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011, 33(11): 2259-2272.
[5] YANG Hanxue, SHAO Ling, ZHENG Feng, et al. Recent advances and trends in visual tracking: a review[J]. Neurocomputing, 2011, 74(18): 3823-3831.
[6] 常发亮, 马丽, 刘增晓, 等. 复杂环境下基于自适应粒子滤波器的目标跟踪[J]. 电子学报, 2006, 34(12): 2150-2153. CHANG Faliang, MA Li, LIU Zengxiao, et al. Target tracking based on adaptive particle filter under complex background[J]. Acta Electronical Sinica, 2006, 34(12): 2150-2153.
[7] 齐美彬, 张莉, 蒋建国, 等. 分块跟踪中的目标模板更新方法[J]. 中国图象图形学报, 2011, 16(6): 976-982. QI Meibin, ZHANG Li, JIANG Jianguo, et al. Target template update method in fragment tracking[J]. Journal of Image and Graphics, 2011, 16(6): 976-982.
[8] 牛长锋, 陈登峰, 刘玉树. 基于SIFT特征和粒子滤波的目标跟踪方法[J]. 机器人, 2010, 32(2): 241-247. NIU Changfeng, CHEN Dengfeng, LIU Yushu. Tacking object based on SIFT features and particle filter[J]. Robot, 2010, 32(2): 241-247.
[9] 左军毅, 张怡哲, 梁彦. 自适应不完全重采样粒子滤波器[J]. 自动化学报, 2012, 38(4): 647-652. ZUO Junyi, ZHANG Yizhe, LIANG Yan. Particle filter based on adaptive part resampling[J]. Acta Automatica Sinica, 2012, 38(4): 647-652.
[10] FLAVELL J H. Metacognitive aspects of problem solving[M]//RESNICK L B. The Nature of Intelligence. Hillsdale, USA: Erlbaum, 1976: 231-232.
[11] 董奇. 论元认知[J]. 北京师范大学学报, 1989(1): 68-74.
[12] 冯惠昌. 元认知与学习[J]. 内蒙古教育学院学报, 1992(3): 66-70.
[13] 杜晓新. 元认知在认知活动中的作用—兼论如何培养学生的元认知能力[J]. 上海师范大学学报, 1992(3): 135-139.
[14] 汪玲, 郭德俊. 元认知的本质与要素[J]. 心理学报, 2000, 32(4): 458-463. WANG Ling, GUO Dejun. The nature and components of metacognition[J]. Acta Psychological Sinica, 2000, 32(4): 458-463.
[15] 杨宁. 元认知研究的理论意义[J]. 心理学报, 1995, 27(3): 322-328. YANG Ning. Theoretical meaning of the study on metacognition[J]. Acta Psychological Sinica, 1995, 27(3): 322-328.
[16] 董文会, 常发亮, 李天平. 融合颜色直方图及SIFT特征的自适应分块目标跟踪方法[J]. 电子与信息学报, 2013, 35(4): 770-776. DONG Wenhui, CHANG Faliang, LI Tianping. Adaptive fragments-based target tracking method fusing color histogram and SIFT features[J]. Journal of Electronics & Information Technology, 2013, 35(4): 770-776.
[17] 陈真, 王钊. 基于元认知模型的智能混合高斯背景建模[J]. 计算机系统应用. 2013, 22(9): 180-184, 159. CHEN Zhen, WANG Zhao. Metacognitive model-based intelligent Gaussian mixture background modeling[J]. Computer Systems & Applications, 2013, 22(9): 180-184, 159.

相似文献/References:

[1]伍 明,孙继银.一种机器人未知环境下动态目标跟踪交互多模滤波算法[J].智能系统学报,2010,5(02):127.
 WU Ming,SUN Ji-yin.An interacting multiple model filtering algorithm for mobile robots to improve tracking of moving objects in unknown environments[J].CAAI Transactions on Intelligent Systems,2010,5(03):127.
[2]杨 戈,刘 宏.视觉跟踪算法综述[J].智能系统学报,2010,5(02):95.
 YANG Ge,LIU Hong.Survey of visual tracking algorithms[J].CAAI Transactions on Intelligent Systems,2010,5(03):95.
[3]李 金,胡文广.基于颜色的快速人体跟踪及遮挡处理[J].智能系统学报,2010,5(04):353.
 LI Jin,HU Wen-guang.Tracking fast movement using colors while accommodating occlusion[J].CAAI Transactions on Intelligent Systems,2010,5(03):353.
[4]海 丹,李 勇,张 辉,等.无线传感器网络环境下基于粒子滤波的移动机器人SLAM算法[J].智能系统学报,2010,5(05):425.[doi:10.3969/j.issn.1673-4785.2010.05.008]
 HAI Dan,LI Yong,ZHANG Hui,et al.Simultaneous localization and mapping of a mobile robot in wireless sensor networks based on particle filtering[J].CAAI Transactions on Intelligent Systems,2010,5(03):425.[doi:10.3969/j.issn.1673-4785.2010.05.008]
[5]韩华,丁永生,郝矿荣.综合颜色和小波纹理特征的免疫粒子滤波视觉跟踪[J].智能系统学报,2011,6(04):289.
 HAN Hua,DING Yongsheng,HAO Kuangrong.An immune particle filter video tracking method based on color and wavelet texture[J].CAAI Transactions on Intelligent Systems,2011,6(03):289.
[6]刘侠,陶冶,邢春.统计差分与自启动的Camshift跟踪算法[J].智能系统学报,2011,6(04):355.
 LIU Xia,TAO Ye,XING Chun.An objective tracking Camshift algorithm based onautomatic startup and the statistical differential method[J].CAAI Transactions on Intelligent Systems,2011,6(03):355.
[7]伍明,孙继银.基于粒子滤波的未知环境下机器人同时定位、地图构建与目标跟踪[J].智能系统学报,2013,8(02):168.[doi:10.3969/j.issn.1673-4785.201202001]
 WU Ming,SUN Jiyin.Simultaneous localization, mapping and object tracking in an unknown environment using particle filtering[J].CAAI Transactions on Intelligent Systems,2013,8(03):168.[doi:10.3969/j.issn.1673-4785.201202001]
[8]贺超,刘华平,孙富春,等.采用Kinect的移动机器人目标跟踪与避障[J].智能系统学报,2013,8(05):426.[doi:10.3969/j.issn.1673-4785.201301028]
 HE Chao,LIU Huaping,SUN Fuchun,et al.Target tracking and obstacle avoidance of mobile robot using Kinect[J].CAAI Transactions on Intelligent Systems,2013,8(03):426.[doi:10.3969/j.issn.1673-4785.201301028]
[9]王熙,吴为,钱沄涛.基于轨迹聚类的超市顾客运动跟踪[J].智能系统学报,2015,10(02):187.[doi:10.3969/j.issn.1673-4785.201401002]
 WANG Xi,WU Wei,QIAN Yuntao.Trajectory clustering based customer movement tracking in a supermarket[J].CAAI Transactions on Intelligent Systems,2015,10(03):187.[doi:10.3969/j.issn.1673-4785.201401002]
[10]王尔申,李兴凯,庞涛.基于BP神经网络的粒子滤波算法[J].智能系统学报,2014,9(06):709.[doi:10.3969/j.issn.1673-4785.201310057]
 WANG Ershen,LI Xingkai,PANG Tao.A particle filtering algorithm based on the BP neural network[J].CAAI Transactions on Intelligent Systems,2014,9(03):709.[doi:10.3969/j.issn.1673-4785.201310057]
[11]王绍钰 蔡自兴,陈爱斌.改进的粒子滤波器目标跟踪方法[J].智能系统学报,2008,3(03):189.
 WANG Shao-yu,CAI Zi-xing,CHEN Ai-bin.Improved object tracking method for particle filters[J].CAAI Transactions on Intelligent Systems,2008,3(03):189.
[12]刘 清,吴志刚,窦 琴,等.粒子滤波的视频目标跟踪算法研究[J].智能系统学报,2009,4(06):538.[doi:10.3969/j.issn.1673-4785.2009.06.012]
 LIU Qing,WU Zhi-gang,DOU Qin,et al.A particle filtering algorithm for tracking moving objects in videos[J].CAAI Transactions on Intelligent Systems,2009,4(03):538.[doi:10.3969/j.issn.1673-4785.2009.06.012]
[13]刘峰,宣士斌,刘香品.佳点集的QMC粒子滤波算法及其应用[J].智能系统学报,2014,9(04):461.[doi:10.3969/j.issn.1673-4785.201305079]
 LIU Feng,XUAN Shibin,LIU Xiangpin.Quasi-Monte Carlo particle filter algorithm based on the good point set and its application[J].CAAI Transactions on Intelligent Systems,2014,9(03):461.[doi:10.3969/j.issn.1673-4785.201305079]
[14]许奇,王华彬,周健,等.用于目标跟踪的智能群体优化滤波算法[J].智能系统学报,2019,14(04):697.[doi:10.11992/tis.201805049]
 XU Qi,WANG Huabin,ZHOU Jian,et al.Swarm intelligence filtering for robust object tracking[J].CAAI Transactions on Intelligent Systems,2019,14(03):697.[doi:10.11992/tis.201805049]

备注/Memo

备注/Memo:
收稿日期:2014-5-23;改回日期:。
基金项目:山东省优秀中青年科学家科研奖励基金(BS2011DX040);中央高校基本科研业务费专项资金(11CX04045A).
作者简介:陈真,女,1975年生,高级工程师,主要研究方向为信号处理、模式识别、人工智能.王钊,男,1976年生,副教授,主要研究方向为非线性系统.
通讯作者:陈真. E-mail: chen_zhen_09@163.com.
更新日期/Last Update: 2015-07-15