Please wait a minute...
浙江大学学报(工学版)
J4  2010, Vol. 44 Issue (6): 1091-1097    DOI: 10.3785/j.issn.1008-973X.2010.06.007
自动化技术、计算机技术     
多摄像机间基于最稳定极值区域的人体跟踪方法
张莉1,2, 刘济林1
1.浙江大学 信息与电子工程学系,浙江 杭州 310027; 2.杭州电子科技大学 计算机学院,浙江 杭州 310018
Human tracking method based on maximally stable extremal regions with multicameras
ZHANG Li1,2,LIU Ji-lin1
1. School of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China; 2. School of Computer Science, Hangzhou Dianzi University, Hangzhou 310018, China
 全文: PDF  HTML
摘要：

为实现多摄像机视频监控系统中的人体跟踪,提出了一种以最稳定极值区域作为匹配特征的人体跟踪方法.该方法使用图像的灰度信息,在一定程度上减小了摄像机增益及光谱特性对人体跟踪造成的影响.通过把人体跟踪转化为椭圆形区域的匹配,算法将最稳定极值区域拟合成椭圆形区域,在此基础上,选出符合约束条件的候选椭圆形区域,并将其归一化为单位圆形区域,通过在圆形区域内计算旋转不变量、进行直方图密度估计和计算加权平均距离实现椭圆形区域的正确匹配,从而实现多摄像机间的人体跟踪.实验结果表明：该算法可有效实现多摄像机间的人体跟踪.

 
Abstract:

A human tracking method based on maximally stable extremal region（MSER）was established in order to realize the human tracking with multicameras in the video surveillance system. The proposed method uses gray information to reduce the difference of the cameras’ gains and their light spectrum property. The approach transforms the human tracking into elliptic region matching. The method does elliptic region fitting to each maximally stable extremal region(MSER), and then selects the elliptic regions which meet some constraints. These selected elliptic regions are normalized to unity circular regions by whitening of covariance matrix. The right matched elliptic regions are obtained by rotational invariant vectors calculation, histogram density estimation and weighted average distance calculation. Thus, the human tracking across cameras is realized. Experimental results show that the approach can effectively realize the human tracking with multi-cameras.
出版日期: 2010-07-16
:  TP 391.41  
基金资助:

国家自然科学基金资助项目（60534070）
通讯作者: 刘济林，男，教授.     E-mail: liujl@zju.edu.cn
作者简介: 张莉(1979—)，女，河南永城人，讲师，博士，从事模式识别研究.E-mail: dr.zhangliangie@yahoo.com.cn
服务　　
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章　　

引用本文:

张莉, 刘济林. 多摄像机间基于最稳定极值区域的人体跟踪方法[J]. J4, 2010, 44(6): 1091-1097.

ZHANG Chi, LIU Ji-Lin. Human tracking method based on maximally stable extremal regions with multicameras. J4, 2010, 44(6): 1091-1097.

链接本文:

http://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2010.06.007    或    http://www.zjujournals.com/eng/CN/Y2010/V44/I6/1091

［1］ JAVED O, RASHEED Z, SHAFIQUE K, et al. Tracking across multiple cameras with disjoint views［C］∥Proceedings of the Ninth IEEE International Conference on Computer Vision. Nice : IEEE, 2003: 952957.


［2］ KHAN S, SHAN M. Consistent labeling of tracked objects in multiple cameras with overlapping fields of view［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003, 25(10): 13551360.

［3］ FRAUNDORFER F, BISCHOF H. Evaluation of local detectors on nonplanar scenes［C］∥Proceedings of the 28th Workshop of the Austrian Association for Pattern Recognition. Hagenberg: IEEE, 2004: 125132.

［4］ MIKOLAJCZYK K, TUYTELAARS T, SCHMID C, et al. A comparison of affine region detectors［J］. International Journal of Computer Vision, 2005, 65 (1/2): 4372.

［5］ MOREELS P, PERONA P. Evaluation of features detectors and descriptors based on 3D objects［C］∥Proceedings of International Conference on Computer Vision. Beijing: IEEE, 2005: 800807.

［6］ /MATAS J, CHUM O, URBAN M, et al. Robust wide baseline stereo from maximally stable extremal regions［J］. Image and Vision Computing, 2004, 22(10): 761767.

［7］ MATAS J, BILEK P, CHUM O. Rotational invariants for widebaseline stereo ［EB/OL］. ［ 20070614］.ftp:∥cmp.felk.cvut.cz/pub/cmp/articles/bilek/MatasBilekCVWW02.pdf.

［8］ PANERO J, ZELNIK M. Human dimension and interior space (Hardcover)［M］. USA: WatsonGuptill Publications, 1979.

［9］ LOWE D G. Distinctive image features from scaleinvariant keypoints［J］. International Journal of Computer Vision， 2004, 60(2): 91110.
[1] 杨玉婷, 史玉回, 夏顺仁. 基于讨论机制的头脑风暴优化算法[J]. J4, 2013, 47(10): 1705-1711.
[2] 朱晓恩, 郝欣, 夏顺仁. 基于Levy flight的特征选择算法[J]. J4, 2013, 47(4): 638-643.
[3] 孙创日,甄帅,夏顺仁. 基于吸引区域的多模态脑磁共振图像仿射配准[J]. J4, 2012, 46(9): 1722-1728.
[4] 谢迪, 童若锋, 唐敏, 冯阳. 具有高区分度的视频火焰检测方法[J]. J4, 2012, 46(4): 698-704.
[5] 戴渊明, 韦巍, 林亦宁. 基于颜色纹理特征的均值漂移目标跟踪算法[J]. J4, 2012, 46(2): 212-217.
[6] 李启雷, 金文光, 耿卫东. 基于无线惯性传感器的人体动作捕获方法[J]. J4, 2012, 46(2): 280-285.
[7] 刘晨彬,潘颖,张海石,黄峰平,夏顺仁. 基于磁共振图像的脑瘤MGMT表达状况检测算法[J]. J4, 2012, 46(1): 170-176.
[8] 钱诚, 张三元. 适用于目标跟踪的加权增量子空间学习算法[J]. J4, 2011, 45(12): 2240-2246.
[9] 曹颖, 郝欣, 朱晓恩, 夏顺仁. 基于自动随机游走的乳腺肿块分割算法[J]. J4, 2011, 45(10): 1753-1760.
[10] 吕谷来, 李建平, 李锵, 俞利兴, 朱松明, 楼建忠, 袁祎琳. 基于机器视觉的砧木定位识别方法[J]. J4, 2011, 45(10): 1766-1770.
[11] 赖小波,朱世强. 基于互相关信息的非参数变换立体匹配算法[J]. J4, 2011, 45(9): 1636-1642.
[12] 刘建明, 鲁东明, 葛蓉. 基于全局优化的图像修复及其在GPU上实现[J]. J4, 2011, 45(2): 247-252.
[13] 王金德, 寿黎但, 李晓燕, 陈刚. 基于多重分割捆绑特征的目标图像检索[J]. J4, 2011, 45(2): 259-266.
[14] 梁文锋,项志宇. 鲁棒的PTZ摄像机目标跟踪算法[J]. J4, 2011, 45(1): 59-63.
[15] 战江涛,刘强,柴春雷. 基于三维模型与Gabor小波的人脸特征点跟踪方法[J]. J4, 2011, 45(1): 30-36.