Robust object tracking via online multiple instance metric learning

Min Yang; Caixia Zhang; Yuwei Wu; Mingtao Pei; Yunde Jia

doi:10.1109/ICMEW.2013.6618252

Robust object tracking via online multiple instance metric learning

Min Yang, Caixia Zhang, Yuwei Wu, Mingtao Pei, Yunde Jia

Beijing Institute of Technology

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

8 引用（Scopus）

摘要

This paper presents a novel object tracking method using online multiple instance metric learning to adaptively capture appearance variations. More specifically, we seek for an appropriate metric via online metric learning to match the different appearances of an object and simultaneously separate the object from the background. The drift problem caused by potentially misaligned training examples is alleviated by performing online metric learning under the multiple instance setting. Both qualitative and quantitative evaluations on various challenging sequences are discussed.

源语言	英语
主期刊名	Electronic Proceedings of the 2013 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2013
DOI	https://doi.org/10.1109/ICMEW.2013.6618252
出版状态	已出版 - 2013
活动	2013 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2013 - San Jose, CA, 美国期限: 15 7月 2013 → 19 7月 2013

出版系列

姓名	Electronic Proceedings of the 2013 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2013

会议

会议	2013 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2013
国家/地区	美国
市	San Jose, CA
时期	15/07/13 → 19/07/13

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10.1109/ICMEW.2013.6618252

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Yang, M., Zhang, C., Wu, Y., Pei, M., & Jia, Y. (2013). Robust object tracking via online multiple instance metric learning. 在 Electronic Proceedings of the 2013 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2013 文章 6618252 (Electronic Proceedings of the 2013 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2013). https://doi.org/10.1109/ICMEW.2013.6618252

@inproceedings{cdcce4c9ded5472d95f6b63db3ea8832,

title = "Robust object tracking via online multiple instance metric learning",

abstract = "This paper presents a novel object tracking method using online multiple instance metric learning to adaptively capture appearance variations. More specifically, we seek for an appropriate metric via online metric learning to match the different appearances of an object and simultaneously separate the object from the background. The drift problem caused by potentially misaligned training examples is alleviated by performing online metric learning under the multiple instance setting. Both qualitative and quantitative evaluations on various challenging sequences are discussed.",

keywords = "appearance variation, multiple instance, object tracking, online metric learning",

author = "Min Yang and Caixia Zhang and Yuwei Wu and Mingtao Pei and Yunde Jia",

year = "2013",

doi = "10.1109/ICMEW.2013.6618252",

language = "English",

isbn = "9781479916047",

series = "Electronic Proceedings of the 2013 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2013",

booktitle = "Electronic Proceedings of the 2013 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2013",

note = "2013 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2013 ; Conference date: 15-07-2013 Through 19-07-2013",

}

Yang, M, Zhang, C, Wu, Y, Pei, M & Jia, Y 2013, Robust object tracking via online multiple instance metric learning. 在 Electronic Proceedings of the 2013 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2013., 6618252, Electronic Proceedings of the 2013 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2013, 2013 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2013, San Jose, CA, 美国, 15/07/13. https://doi.org/10.1109/ICMEW.2013.6618252

Robust object tracking via online multiple instance metric learning. / Yang, Min; Zhang, Caixia; Wu, Yuwei 等.
Electronic Proceedings of the 2013 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2013. 2013. 6618252 (Electronic Proceedings of the 2013 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2013).

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

TY - GEN

T1 - Robust object tracking via online multiple instance metric learning

AU - Yang, Min

AU - Zhang, Caixia

AU - Wu, Yuwei

AU - Pei, Mingtao

AU - Jia, Yunde

PY - 2013

Y1 - 2013

N2 - This paper presents a novel object tracking method using online multiple instance metric learning to adaptively capture appearance variations. More specifically, we seek for an appropriate metric via online metric learning to match the different appearances of an object and simultaneously separate the object from the background. The drift problem caused by potentially misaligned training examples is alleviated by performing online metric learning under the multiple instance setting. Both qualitative and quantitative evaluations on various challenging sequences are discussed.

AB - This paper presents a novel object tracking method using online multiple instance metric learning to adaptively capture appearance variations. More specifically, we seek for an appropriate metric via online metric learning to match the different appearances of an object and simultaneously separate the object from the background. The drift problem caused by potentially misaligned training examples is alleviated by performing online metric learning under the multiple instance setting. Both qualitative and quantitative evaluations on various challenging sequences are discussed.

KW - appearance variation

KW - multiple instance

KW - object tracking

KW - online metric learning

UR - http://www.scopus.com/inward/record.url?scp=84888212593&partnerID=8YFLogxK

U2 - 10.1109/ICMEW.2013.6618252

DO - 10.1109/ICMEW.2013.6618252

M3 - Conference contribution

AN - SCOPUS:84888212593

SN - 9781479916047

T3 - Electronic Proceedings of the 2013 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2013

BT - Electronic Proceedings of the 2013 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2013

T2 - 2013 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2013

Y2 - 15 July 2013 through 19 July 2013

ER -

Yang M, Zhang C, Wu Y, Pei M, Jia Y. Robust object tracking via online multiple instance metric learning. 在 Electronic Proceedings of the 2013 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2013. 2013. 6618252. (Electronic Proceedings of the 2013 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2013). doi: 10.1109/ICMEW.2013.6618252

Robust object tracking via online multiple instance metric learning

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