- Title
- Siamese network for object tracking with multi-granularity appearance representations
- Creator
- Zhang, Zhuoyi; Zhang, Yifeng; Cheng, Xu; Lu, Guojun
- Date
- 2021
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/178545
- Identifier
- vital:15457
- Identifier
-
https://doi.org/10.1016/j.patcog.2021.108003
- Identifier
- ISBN:0031-3203 (ISSN)
- Abstract
- A reliable tracker has the ability to adapt to change of objects over time, and is robust and accurate. We build such a tracker by extracting semantic features using robust Siamese networks and multi-granularity color features. It incorporates a semantic model that can capture high quality semantic features and an appearance model that can describe object at pixel, local and global levels effectively. Furthermore, we propose a novel selective traverse algorithm to allocate weights to semantic models and appearance models dynamically for better tracking performance. During tracking, our tracker updates appearance representations for objects based on the recent tracking results. The proposed tracker operates at speeds that exceed the real-time requirement, and outperforms nearly all other state-of-the-art trackers on OTB-2013/2015 and VOT-2016/2017 benchmarks. © 2021 Elsevier Ltd
- Publisher
- Elsevier Ltd
- Relation
- Pattern Recognition Vol. 118, no. (2021), p.
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright © 2021 Elsevier Ltd
- Subject
- 0801 Artificial Intelligence and Image Processing; 0806 Information Systems; 0906 Electrical and Electronic Engineering; Appearance adaption; Object tracking; Siamese network
- Reviewed
- Funder
- This work was supported in part by the Natural Science Foundation of Jiangsu Province under Grants BK20151102 and BK20201267 , and in part by the Natural Science Foundation of China under Grants 61673108 and 61802058 .
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