Robust Object Tracking using Probabilistic Hypergraph Ranking and Superpixels

DOI

10.2991/ifmeita-16.2016.134How to use a DOI?

Keywords

Visual tracking, transductive learning, hypergraph ranking, superpixel.

Abstract

Online object tracking is a challenging issue because the appearance of an object tends to change due to intrinsic or extrinsic factors. In this study, we propose a robust tracking algorithm based on probabilistic hypergraph ranking and superpixels. The probabilistic hypergraph is constructed by mid-level visual cues and their spatial relationships. Then, the confidence map at mid-level cues is obtained by hypergraph ranking analysis, which takes the high order intrinsic relationships of superpixels into account. Third, Object tracking is formulated as a transductive learning issue, and the optimal target location is determined by maximum a posterior estimation on the ranking scores. Finally, a dynamic updating scheme is proposed to address appearance variations and alleviate tracking drift. A series of experiments and evaluations on various challenging sequences are performed, and the results show that the proposed algorithm performs favorably against other existing state-of-the-art methods.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Ruitao Lu
AU  - Wanying Xu
AU  - Yongbin Zheng
AU  - Shengjian Bai
AU  - Xinsheng Huang
PY  - 2016/01
DA  - 2016/01
TI  - Robust Object Tracking using Probabilistic Hypergraph Ranking and Superpixels
BT  - Proceedings of the 2016 International Forum on Management, Education and Information Technology Application
PB  - Atlantis Press
SP  - 734
EP  - 739
SN  - 2352-5398
UR  - https://doi.org/10.2991/ifmeita-16.2016.134
DO  - 10.2991/ifmeita-16.2016.134
ID  - Lu2016/01
ER  -