@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",
}