[1]刘翠君,赵才荣,苗夺谦,等.粒化的Mean Shift行人跟踪算法[J].智能系统学报,2016,11(4):433-441.[doi:10.11992/tis.201605033]
 LIU Cuijun,ZHAO Cairong,MIAO Duoqian,et al.Granular mean shift pedestrian tracking algorithm[J].CAAI Transactions on Intelligent Systems,2016,11(4):433-441.[doi:10.11992/tis.201605033]
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第11卷
期数:
2016年4期
页码:
433-441
栏目:
出版日期:
2016-07-25

文章信息/Info

Title:
Granular mean shift pedestrian tracking algorithm
作者:
刘翠君 赵才荣 苗夺谦 王学宽
同济大学 电子与信息工程学院, 上海 201804
Author(s):
LIU Cuijun ZHAO Cairong MIAO Duoqian WANG Xuekuan
College of Electronics and Information Engineering, Tongji University, Shanghai 201804, China
关键词:
信息粒粒计算Mean Shift特征提取行人跟踪
Keywords:
information granulesgranular computingmean shiftfeature extractionpedestrian tracking
分类号:
TP391
DOI:
10.11992/tis.201605033
摘要:
Mean Shift行人跟踪采用颜色特征直方图作为跟踪特征,存在易受背景颜色干扰等问题。基于此,在传统的Mean Shift行人跟踪算法中引入粒计算的思想,提出粒化的Mean Shift行人跟踪算法,对图像目标区域作粒层分块来提取块颜色特征信息,并在颜色特征表示上作不同粒度的粒化,最后在Mean Shift迭代框架下实现行人跟踪。该方法相比传统的跟踪方法具有计算复杂度更低、稳健性更好的优点。在PETS2009和CAVIAR数据库上的实验表明,这种方法跟踪正确率更高,在颜色干扰下稳健性更好,能够实时有效地跟踪行人。
Abstract:
Mean shift pedestrian tracking that uses a color histogram as its tracking feature has drawbacks, e.g., performance can easily be affected by the introduction of a background color. To solve this problem, the idea of granular computing was introduced into the traditional mean shift pedestrian tracking algorithm, and a new granular mean shift pedestrian tracking algorithm, based on granular computing, is presented. The algorithm blocks the image’s target area with specific granularity to extract color features, then adopts different color channels of granulation on the feature, and finally realizes target tracking under the framework of the mean shift iteration. Compared with other traditional methods the algorithm displays lower computational complexity and is more robust. Experimental results on PETS2009 and CAVIAR databases show that the algorithm achieves a higher tracking accuracy, better robustness and efficiency under color interference, and can track the target pedestrian in real time.

参考文献/References:

[1] 顾幸方, 茅耀斌, 李秋洁. 基于Mean Shift的视觉目标跟踪算法综述[J]. 计算机科学, 2012, 39(12):16-24. GU Xingfang, MAO Yaobin, LI Qiujie. Survey on visual tracking algorithms based on mean shift[J]. Computer science, 2012, 39(12):16-24.
[2] COMANICIU D, RAMESH V, MEER P. Kernel-based object tracking[J]. IEEE transactions on pattern analysis and machine intelligence, 2003, 25(5):564-577.
[3] KHAN Z H, GU Yuhua, BACKHOUSE A G. Robust visual object tracking using multi-mode anisotropic mean shift and particle filters[J]. IEEE transactions on circuits and systems for video technology, 2011, 21(1):74-87.
[4] PENG Ningsong, YANG Jie, LIU Zhi. Mean shift blob tracking with kernel histogram filtering and hypothesis testing[J]. Pattern recognition letters, 2005, 26(5):605-614.
[5] CHEN Aihua, ZHU Ming, WANG Yanhua, et al. Mean shift tracking combining SIFT[C]//Proceedings of the 9th International Conference on Signal Processing. Beijing:IEEE, 2008:1532-1535.
[6] NING Jifeng, ZHANG Lei, ZHANG D, et al. Scale and orientation adaptive mean shift tracking[J]. IET computer vision, 2012, 6(1):52-61.
[7] CHEN Kangli, GE Wancheng. Pedestrian tracking algorithm based on kalman filter and partial mean-shift tracking[C]//Proceedings of the 2nd International Conference on Systems and Informatics. Shanghai:IEEE, 2014:230-235.
[8] FANG Jiangxiong, YANG Jie, LIU Huaxiang. Efficient and robust fragments-based multiple kernels tracking[J]. AEU-international journal of electronics and communications, 2011, 65(11):915-923.
[9] SHEN Chunhua, BROOKS M J, VAN DEN HENGEL A. Fast global kernel density mode seeking:applications to localization and tracking[J]. IEEE transactions on image processing, 2007, 16(5):1457-1469.
[10] CHANG S. Stochastic peak tracking and the Kalman filter[J]. IEEE transactions on automatic control, 1968, 13(6):750.
[11] DOUCET A, GORDON N. Efficient particle filters for tracking manoeuvring targets in clutter[C]//Proceedings of IEE Colloquium on Target Tracking:Algorithms and Applications. London:IEEE, 1999:4/1-4/5.
[12] ZADEH L A. Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic[J]. Fuzzy sets and systems, 1997, 90(2):111-127.
[13] LIN T Y. Granular computing:fuzzy logic and rough sets[C]//ZADEH L A, KACPRZYK J. Computing with Words in Information/Intelligent Systems 1. Berlin Heidelberg:Physica-Verlag HD, 1999:183-200.
[14] 苗夺谦, 王国胤, 刘清, 等. 粒计算:过去、现在与展望[M]. 北京:科学出版社, 2007. MIAO Duoqian, WANG Guoyin, LIU Qing, et al. Granular computing:past, present and future[M]. Beijing:Science Press, 2007.
[15] 徐计, 王国胤, 于洪. 基于粒计算的大数据处理[J]. 计算机学报, 2015, 38(8):1497-1517. XU Ji, WANG Guoyin, YU Hong. Review of big data processing based on granular computing[J]. Chinese journal of computers, 2015, 38(8):1497-1517.

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备注/Memo

备注/Memo:
收稿日期:2016-05-30。
基金项目:国家自然科学基金项目(61273304);上海市中医药三年行动计划重点项目(ZY3-CCCX-3-6002)
作者简介:刘翠君,女,1993年生,硕士研究生,主要研究方向为计算机视觉、粒计算等;赵才荣,男,1981年生,副研究员,博士,主要研究方向为人脸识别、计算机视觉等;苗夺谦,男,1964年生,教授、博士生导师,博士,主要研究方向为粒计算、粗糙集、中文信息处理等。
通讯作者:苗夺谦.E-mail:dqmiao@tongji.edu.cn.
更新日期/Last Update: 1900-01-01