基于支持矢量机和信任域的目标跟踪算法

北京大学学报（自然科学版）

基于支持矢量机和信任域的目标跟踪算法

贾静平¹,张飞舟¹,柴艳妹²

¹北京大学遥感与地理信息系统研究所，北京100871；²清华大学计算机科学与技术系，北京100084；

收稿日期:2007-09-06 出版日期:2008-09-20 发布日期:2008-09-20

Object Tracking by Using SVM and Trust-Region Method

JIA Jingping¹ ZHANG Feizhou¹ CHAI Yanmei²

¹Institute of Remote Sensing and Geographic Information System, Peking University, Beijing 100871; ²Department of Computer Science and Technology, Tsinghua University, Beijing 100084;

Received:2007-09-06 Online:2008-09-20 Published:2008-09-20

摘要/Abstract

摘要： 通过使用SVM(支持矢量机)分类器对像素分类进行目标检测，将输入图像转换成可靠的目标概率分布图，然后结合使用性能优良的信任域优化算法，在概率分布图上实现目标定位并确定其尺寸。分类器对像素分类的低错分率为信任域算法提供了更好的基础。通过和现有算法的比较以及在真实序列图像上的实验表明，该算法不但能够更准确地检测到目标，而且在跟踪精度方面有了显著提高。

关键词: 支持矢量机, 尺度空间理论, 信任域算法

Abstract: An SVM (support vector machine) classifier is used to classify the pixels and generate a reliable target probability distribution image The outstanding trust region numeric optimization algorithm is utilized to locate the target as well as determine its size The SVM classifier's low error rate of classification offers a better base for the trust region algorithm Comparison with existing method and present tracking examples show that the proposed method is better in both target detection and precision improvement

Key words: support vector machine, scale-space theory, trust region algorithm

中图分类号:

TP391

贾静平,张飞舟,柴艳妹. 基于支持矢量机和信任域的目标跟踪算法[J]. 北京大学学报（自然科学版）.

JIA Jingping,ZHANG Feizhou,CHAI Yanmei. Object Tracking by Using SVM and Trust-Region Method[J]. Acta Scientiarum Naturalium Universitatis Pekinensis.

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链接本文: https://xbna.pku.edu.cn/CN/

https://xbna.pku.edu.cn/CN/Y2008/V44/I5/810