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	Adaptive visual tracking with reacquisition ability for arbitrary objects
	Tianyu Yang; Baopu Li; Chao Hu; Max Q.-H. Meng
	2013
会议名称	2013 IEEE International Conference on Robotics and Automation, ICRA 2013
会议地点	Karlsruhe, Germany
英文摘要	This paper introduces a novel tracking framework for robots that can adapt various appearance changes of object and also owns the ability of reacquisition after drift. Two classifiers, LaRank and Online Random Ferns, are adopted to realize this tracking algorithm. The former one maintains the adaptive tracking using a Condensation-based method with an online support vector machine (SVM) as observation model, which also provides the reliable image patch samples to detector for updating. The other one is in charge of the task of detection in order to redetect the object when the target drifts. We also present a refinement strategy to improve the tracker's performance by discarding the support vector corresponding to possible wrong updates by a matching template after re-initialization. The experiments on benchmark dataset compare our tracking method with several other state-of-the-art algorithms, demonstrating a promising performance of the proposed framework.
收录类别	EI
语种	英语
内容类型	会议论文
源URL	[http://ir.siat.ac.cn:8080/handle/172644/4535]
专题	深圳先进技术研究院_集成所
作者单位	2013
推荐引用方式 GB/T 7714	Tianyu Yang,Baopu Li,Chao Hu,et al. Adaptive visual tracking with reacquisition ability for arbitrary objects[C]. 见:2013 IEEE International Conference on Robotics and Automation, ICRA 2013. Karlsruhe, Germany.

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