@inproceedings{2beddd16166742aba9af23c5b7beb1ea,
title = "Speeded up visual tracker with adaptive template updating method",
abstract = "Tracking an object with limited prior information regarding to its appearance is a challenging problem that attracts much attention. In this paper, we propose a speeded up visual tracker that is not only capable of long-term tracking but also of online tasks. The tracker treats object tracking as a binary classification problem between the object and background information. Usually, little information is available for training in real cases, which makes trackers with pre-defined distance metric to drift. To solve this problem, the proposed tracker adopts distance metric learning to update classifier after every frame for a more robust tracking result. We use dense SIFT feature to describe an object appearance and randomized principle component analysis (RPCA) to reduce the original feature space dimensionality. Additionally, a new partially-updated template library is proposed for a more robust tracking. The experiment results show that the proposed tracker performs preferable comparing to state-of-art trackers.",
keywords = "Feature projection, Metric learning, Template updating, Visual tracking",
author = "Shuqiao Sun and Wenjing Kang and Gongliang Liu",
note = "Publisher Copyright: {\textcopyright} 2019, Springer Nature Singapore Pte Ltd.; 6th International Conference on Communications, Signal Processing, and Systems, CSPS 2017 ; Conference date: 14-07-2017 Through 16-07-2017",
year = "2019",
doi = "10.1007/978-981-10-6571-2\_333",
language = "英语",
isbn = "9789811065705",
series = "Lecture Notes in Electrical Engineering",
publisher = "Springer Verlag",
pages = "2754--2761",
editor = "Qilian Liang and Min Jia and Jiasong Mu and Wei Wang and Xuhong Feng and Baoju Zhang",
booktitle = "Communications, Signal Processing, and Systems - Proceedings of the 2017 International Conference on Communications, Signal Processing, and Systems",
address = "德国",
}