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Improved Semi-supervised Online Boosting for Object Tracking
Li YC(李义翠); Qi L(亓琳); Tan SK(谭舒昆)
作者部门光电信息技术研究室
会议名称International Symposium on Infrared Technology and Application and the International Symposiums on Robot Sensing and Advanced Control
会议日期May 9-11, 2016
会议地点Beijing
会议录名称Proceedings of SPIE - The International Society for Optical Engineering
出版者SPIE
出版地Bellingham, WA
2016
页码1-7
收录类别EI ; CPCI(ISTP)
EI收录号20170503310012
WOS记录号WOS:000391228600104
产权排序1
ISSN号0277-786X
ISBN号978-1-5106-0772-9
关键词Object Tracking Semi-supervised Online Boosting Self-training P-n Constraints
摘要

The advantage of an online semi-supervised boosting method which takes object tracking problem as a classification problem, is training a binary classifier from labeled and unlabeled examples. Appropriate object feature are selected based on real time changes in the object. However, the online semi-supervised boosting method faces one key problem: The traditional self-training using the classification results to update the classifier itself, often leads to drifting or tracking failure, due to the accumulated error during each update of the tracker. To overcome the disadvantages of semi-supervised online boosting based on object tracking methods, the contribution of this paper is an improved online semi-supervised boosting method, in which the learning process is guided by positive (P) and negative (N) constraints, termed P-N constraints, which restrict the labeling of the unlabeled samples. Firstly, we train the classification by an online semi-supervised boosting. Then, this classification is used to process the next frame. Finally, the classification is analyzed by the P-N constraints, which are used to verify if the labels of unlabeled data assigned by the classifier are in line with the assumptions made about positive and negative samples. The proposed algorithm can effectively improve the discriminative ability of the classifier and significantly alleviate the drifting problem in tracking applications. In the experiments, we demonstrate real-time tracking of our tracker on several challenging test sequences where our tracker outperforms other related on-line tracking methods and achieves promising tracking performance.

语种英语
引用统计
文献类型会议论文
条目标识符http://ir.sia.cn/handle/173321/19161
专题光电信息技术研究室
通讯作者Li YC(李义翠)
作者单位1.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110016, China
2.University of Chinese Academy of Sciences, Beijing, 100049, China
推荐引用方式
GB/T 7714
Li YC,Qi L,Tan SK. Improved Semi-supervised Online Boosting for Object Tracking[C]. Bellingham, WA:SPIE,2016:1-7.
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