Original scientific paper

https://doi.org/10.17559/TV-20211115041517

SiamLST: Learning Spatial and Channel-wise Transform for Visual Tracking

Jun Wang ; School of Information Engineering, Nanchang Institute of Technology, Nanchang, China
Limin Zhang ; School of Information Engineering, Nanchang Institute of Technology, Nanchang, China
Yuanyun Wang ; School of Information Engineering, Nanchang Institute of Technology, Nanchang, China
Changwang Lai ; School of Information Engineering, Nanchang Institute of Technology, Nanchang, China
Wenhui Yang ; School of Information Engineering, Nanchang Institute of Technology, Nanchang, China
Chengzhi Deng ; School of Information Engineering, Nanchang Institute of Technology, Nanchang, China

Abstract

Siamese network based trackers regard visual tracking as a similarity matching task between the target template and search region patches, and achieve a good balance between accuracy and speed in recent years. However, existing trackers do not effectively exploit the spatial and inter-channel cues, which lead to the redundancy of pre-trained model parameters. In this paper, we design a novel visual tracker based on a Learnable Spatial and Channel-wise Transform in Siamese network (SiamLST). The SiamLST tracker includes a powerful feature extraction backbone and an efficient cross-correlation method. The proposed algorithm takes full advantages of CNN and the learnable sparse transform module to represent the template and search patches, which effectively exploit the spatial and channel-wise correlations to deal with complicated scenarios, such as motion blur, in-plane rotation and partial occlusion. Experimental results conducted on multiple tracking benchmarks including OTB2015, VOT2016, GOT-10k and VOT2018 demonstrate that the proposed SiamLST has excellent tracking performances.

Keywords

deep learning; siamese network; sparse transform; visual tracking

Hrčak ID:

279467

URI

https://hrcak.srce.hr/279467

Publication date:

17.6.2022.

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