[1]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):697-707.[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(04):697-707.[doi:10.11992/tis.201805049]
点击复制

用于目标跟踪的智能群体优化滤波算法(/HTML)
分享到:

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

卷:
第14卷
期数:
2019年04期
页码:
697-707
栏目:
出版日期:
2019-07-02

文章信息/Info

Title:
Swarm intelligence filtering for robust object tracking
作者:
许奇 王华彬 周健 陶亮
安徽大学 计算智能与信号处理教育部重点实验室, 安徽 合肥 230031
Author(s):
XU Qi WANG Huabin ZHOU Jian TAO Liang
Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, Anhui University, Hefei 230031, China
关键词:
目标跟踪视觉跟踪滤波算法贝叶斯滤波粒子滤波运动模型后验状态智能群体优化
Keywords:
object trackingvisual trackingfiltering algorithmBayesian filterparticle filtermotion modelposterior stateswarm intelligence optimization
分类号:
TP391.41
DOI:
10.11992/tis.201805049
摘要:
针对目标跟踪中的状态估计,提出一种智能群体优化滤波算法。算法在贝叶斯滤波的基础上,运用智能群体优化的3种运动模型估计目标的后验状态,其中内聚运动在保持了粒子多样性的情况下增加了样本的权值,分离运动和排列运动相协调能够更加准确地预测下一时刻目标的先验状态。实验结果表明:与标准粒子滤波相比,该算法能够更加准确地估计非线性系统中的后验状态,在复杂多变的场景环境中,表现出更高的跟踪准确性。
Abstract:
To estimate the state of target in object tracking, a novel algorithm named swarm intelligence filter (SIF) is proposed in this paper. Based on the Bayesian filter, the algorithm could estimate the posterior state using three movements of swarms. The cohesion movement could add the weight by maintaining the diversity of the sample, and the coordination of separation and permutation movements could more accurately predict the state of the next moment compared with the conventional algorithm. The experimental results show that compared with the conventional particle filter, our algorithm could more accurately predict the posterior state in nonlinear systems and more accurately estimate the state of the object in complex environment.

参考文献/References:

[1] LGUENSAT R, TANDEO P, FABLET R, et al. Non-parametric Ensemble Kalman methods for the inpainting of noisy dynamic textures[C]//Proceedings of 2015 IEEE International Conference on Image Processing. Quebec City, Canada, 2016:4288-4292.
[2] 王法胜, 鲁明羽, 赵清杰, 等. 粒子滤波算法[J]. 计算机学报, 2014, 37(8):1679-1694 WANG Fasheng, LU Mingyu, ZHAO Qingjie, et al. Partilce filtering algorithm[J]. Chinese journal of computers, 2014, 37(8):1679-1694
[3] BAO Chenglong, WU Yi, LING Haibin, et al. Real time robust L1 tracker using accelerated proximal gradient approach[C]//Proceedings of 2012 IEEE Conference on Computer Vision and Pattern Recognition. Providence, USA, 2012:1830-1837.
[4] ZHANG Kaihua, LIU Qingshan, WU Yi, et al. Robust visual tracking via convolutional networks without training[J]. IEEE transactions on image processing, 2016, 25(4):1779-1792.
[5] WANG Dong, LU Huchuan. On-line learning parts-based representation via incremental orthogonal projective non-negative matrix factorization[J]. Signal processing, 2013, 93(6):1608-1623.
[6] 吴昊, 孙晓燕, 郭玉堂, 等. 保持粒子多样性的非退化粒子滤波方法研究[J]. 电子学报, 2016, 44(7):1734-1741 WU Hao, SUN Xiaoyan, GUO Yutang, et al. Non-degeneracy particle filtering method research for particle diversity preserving[J]. Acta electronica sinica, 2016, 44(7):1734-1741
[7] 常天庆, 李勇, 刘忠仁, 等. 一种改进重采样的粒子滤波算法[J]. 计算机应用研究, 2013, 30(3):748-750 CHANG Tianqing, LI Yong, LIU Zhongren, et al. Particle filter algorithm based on improved resampling[J]. Application research of computers, 2013, 30(3):748-750
[8] CAO Bei, MA Caiwen, LIU Zhentao. Particle filter with fine resampling for bearings-only tracking[J]. Procedia engineering, 2012, 29:3685-3690.
[9] DU Kelin, SWAMY M N S. Swarm intelligence[M]//DU Kelin, SWAMY M N S. Search and Optimization by Metaheuristics. Cham:Birkhäuser, 2016.
[10] 彭喜元, 彭宇, 戴毓丰. 群智能理论及应用[J]. 电子学报, 2003, 31(S1):1982-1988 PENG Xiyuan, PENG Yu, DAI Yufeng. Swarm intelligence theory and applications[J]. Acta electronica sinica, 2003, 31(S1):1982-1988
[11] CHENG Shi, ZHANG Qingyu, QIN Quande. Big data analytics with swarm intelligence[J]. Industrial management and data systems, 2016, 116(4):646-666.
[12] XIA Junbo. Coverage optimization strategy of wireless sensor network based on swarm intelligence algorithm[C]//Proceedings of 2016 International Conference on Smart City and Systems Engineering. Hunan, China, 2016:179-182.
[13] DEVI K U, SARMA D, LAISHRAM R. Swarm intelligence based computing techniques in speech enhancement[C]//Proceedings of 2015 International Conference on Green Computing and Internet of Things. Noida, India, 2015:1199-1203.
[14] KRONANDER J, SCHÖN T B. Robust auxiliary particle filters using multiple importance sampling[C]//Proceedings of 2014 IEEE Workshop on Statistical Signal Processing. Gold Coast, Australia, 2014:268-271.
[15] WANG Naiyan, SHI Jianping, YEUNG D Y, et al. Understanding and diagnosing visual tracking systems[C]//Proceedings of 2015 IEEE International Conference on Computer Vision. Santiago, Chile, 2015:3101-3109.
[16] 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.
[17] ROSS D A, LIM J, Lin R S, et al. Incremental learning for robust visual tracking[J]. International journal of computer vision, 2008, 77(1/2/3):125-141.
[18] WU Yi, LIM J, YANG M H. Online object tracking:a Benchmark[C]//Proceedings of 2013 IEEE Conference on Computer Vision and Pattern Recognition. Portland, USA, 2013:2411-2418.
[19] JIA Xu, LU Huchuan, YANG M H. Visual tracking via adaptive structural local sparse appearance model[C]//Proceedings of 2012 IEEE Conference on Computer Vision and Pattern Recognition. Providence, USA, 2012:1822-1829.
[20] ORON S, BAR-HILLEL A, LEVI D, et al. Locally orderless tracking[J]. International journal of computer vision, 2015, 111(2):213-228.
[21] ZHANG Tianzhu, GHANEM B, LIU Si, et al. Robust visual tracking via multi-task sparse learning[C]//Proceedings of 2012 IEEE Conference on Computer Vision and Pattern Recognition. Providence, USA, 2012:2042-2049.

相似文献/References:

[1]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(04):189.
[2]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(04):538.[doi:10.3969/j.issn.1673-4785.2009.06.012]
[3]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(04):127.
[4]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(04):95.
[5]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(04):353.
[6]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(04):289.
[7]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(04):355.
[8]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(04):168.[doi:10.3969/j.issn.1673-4785.201202001]
[9]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(04):426.[doi:10.3969/j.issn.1673-4785.201301028]
[10]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(04):187.[doi:10.3969/j.issn.1673-4785.201401002]

备注/Memo

备注/Memo:
收稿日期:2018-05-31。
基金项目:国家自然科学基金项目(61371217);安徽省自然科学基金项目(1708085MF151).
作者简介:许奇,男,1991年生,硕士研究生,主要研究方向为计算机视觉和机器学习;王华彬,男,1983年生,讲师,博士,主要研究方向为手背静脉多模态身份识别、人脸识别、虚拟现实。参与多项国家级和省级科研项目。发表学术论文10余篇;周健,男,1981年生,副教授,博士,安徽省高等学校计算机教育研究会理事,安徽省大学生程序设计竞赛技术委员会副主任委员,主要研究方向为多媒体信息处理、模式识别与机器学习。先后主持安徽省教育厅优秀青年基金项目、安徽大学优秀青年基金项目、安徽省自然科学青年基金项目,以及国家自然科学青年基金项目等。发表学术论文20余篇。
通讯作者:王华彬.E-mail:wanghuabin@ahu.edu.cn
更新日期/Last Update: 2019-08-25