计算机工程与应用2018，Vol.54Issue(13)：203-208,6.DOI:10.3778/j.issn.1002-8331.1703-0004
结合Boosting方法与SVM的多核学习跟踪算法
Multiple-kernel learning based object tracking algorithm with Boosting and SVM
曾礼灵 1李朝锋1
作者信息
- 1. 江南大学 物联网工程学院,江苏 无锡 214122
- 折叠
摘要
Abstract
As traditional tracking algorithms fail to track target stably due to the external environment and the target motion caused deformation, a robust multiple kernel learning based algorithm is proposed. By introducing the Boosting method into the multiple kernel learning framework, building a pool of weak classifiers trained with complementary feature set and complementary kernel function set needs less samples comparing to the traditional multiple-kernel learning algo-rithms. Thus a multiple-kernel strong classifier is constructed by combining several weak classifiers selected from the weak classifier pool, which can correctly differentiate the target and background from the candidate patches even when the target is under notable occlusion and background clutters. Results of test on different video sequences show that when the tracked object is in complex environment, the proposed algorithm has higher tracking accuracy compared with OAB algorithm which similarly uses the Boosting method and the LOT algorithm which has a high tracking accuracy.关键词
多核学习/目标跟踪/提升方法/复杂环境Key words
multiple-kernel learning/object tracking/Boosting method/complex environment分类
信息技术与安全科学引用本文复制引用
曾礼灵,李朝锋..结合Boosting方法与SVM的多核学习跟踪算法[J].计算机工程与应用,2018,54(13):203-208,6.
