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题名:
Improved Semi-supervised Online Boosting for Object Tracking
作者: 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
通讯作者: 李义翠
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: Proceedings of SPIE - The International Society for Optical Engineering
会议录出版者: SPIE
会议录出版地: Bellingham, WA
出版日期: 2016
页码: 1-7
收录类别: EI ; CPCI(ISTP)
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
产权排序: 1
EI收录号: 20170503310012
WOS记录号: WOS:000391228600104
Citation statistics:
内容类型: 会议论文
URI标识: http://ir.sia.cn/handle/173321/19161
Appears in Collections:光电信息技术研究室_会议论文

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Recommended Citation:
Li YC,Qi L,Tan SK. Improved Semi-supervised Online Boosting for Object Tracking[C]. International Symposium on Infrared Technology and Application and the International Symposiums on Robot Sensing and Advanced Control. Beijing. May 9-11, 2016.Improved Semi-supervised Online Boosting for Object Tracking.
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