Densely connected Siamese network visual tracking
ISSN: 0143-991X
Article publication date: 11 June 2021
Issue publication date: 21 September 2021
Abstract
Purpose
Visual object tracking plays a significant role in intelligent robot systems. This study aims to focus on unlocking the tracking performance potential of the deep network and presenting a dynamic template update strategy for the Siamese trackers.
Design/methodology/approach
This paper presents a novel and efficient Siamese architecture for visual object tracking which introduces densely connected convolutional layers and a dynamic template update strategy into Siamese tracker.
Findings
The most advanced performance can be achieved by introducing densely connected convolutional neural networks that have not yet been applied to the tracking task into SiamRPN. By using the proposed architecture, the experimental results demonstrate that the performance of the proposed tracker is 5.8% (area under curve), 5.4% expected average overlap (EAO) and 3.5% (EAO) higher than the baseline on the OTB100, VOT2016 and VOT2018 data sets and achieves an excellent EAO score of 0.292 on the VOT2019 data set.
Originality/value
This study explores a deeper backbone network with each convolutional network layer densely connected. In response to tracking errors caused by templates that are not updated, this study proposes a dynamic template update strategy.
Keywords
Acknowledgements
This work was supported in part by the National Natural Science Foundation of China (NSFC) under Grants 61876168, 61906168 and U1709207, National Key R&D Program of China under Grant 2018YFB1305200, Zhejiang Provincial Natural Science Foundation of China under Grant LY18F030020, and Quzhou Science and Technology Projects under Grants 2019K17 and 2020K19. Xiaolong Zhou and Pinghao Wang contribute equally to this paper.
Citation
Zhou, X., Wang, P., Chan, S., Fang, K. and Fang, J. (2021), "Densely connected Siamese network visual tracking", Industrial Robot, Vol. 48 No. 5, pp. 680-687. https://doi.org/10.1108/IR-01-2021-0010
Publisher
:Emerald Publishing Limited
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