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Distributed and Parallel Databases

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25-05-2018

Online multi-view subspace learning via group structure analysis for visual object tracking

Authors: Wanqi Yang, Yinghuan Shi, Yang Gao, Ming Yang

Published in: Distributed and Parallel Databases | Issue 3/2018

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Abstract

In this paper, we focus on incrementally learning a robust multi-view subspace representation for visual object tracking. During the tracking process, due to the dynamic background variation and target appearance changing, it is challenging to learn an informative feature representation of tracking object, distinguished from the dynamic background. To this end, we propose a novel online multi-view subspace learning algorithm (OMEL) via group structure analysis, which consistently learns a low-dimensional representation shared across views with time changing. In particular, both group sparsity and group interval constraints are incorporated to preserve the group structure in the low-dimensional subspace, and our subspace learning model will be incrementally updated to prevent repetitive computation of previous data. We extensively evaluate our proposed OMEL on multiple benchmark video tracking sequences, by comparing with six related tracking algorithms. Experimental results show that OMEL is robust and effective to learn dynamic subspace representation for online object tracking problems. Moreover, several evaluation tests are additionally conducted to validate the efficacy of group structure assumption.

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All the sequences can be downloaded from the following three web URLs: (1) http://www4.comp.polyu.edu.hk/~cslzhang/CT/CT.htm, (2) http://vision.ucsd.edu/~bbabenko/project_miltrack.html, (3) http://cv.snu.ac.kr/research/~vtd/.

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Title: Online multi-view subspace learning via group structure analysis for visual object tracking
Authors: Wanqi Yang
Yinghuan Shi
Yang Gao
Ming Yang
Publication date: 25-05-2018
Publisher: Springer US
Published in: Distributed and Parallel Databases / Issue 3/2018
Print ISSN: 0926-8782
Electronic ISSN: 1573-7578
DOI: https://doi.org/10.1007/s10619-018-7227-3

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