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

• Published in: Tehnički vjesnik Vol. 29; no. 4; pp. 1202 - 1209
• Main Authors: Wang, Jun, Zhang, Limin, Wang, Yuanyun, Lai, Changwang, Yang, Wenhui, Deng, Chengzhi
• Format: Journal Article Paper
• Language: English
• Published: Slavonski Baod University of Osijek 01.08.2022; Josipa Jurja Strossmayer University of Osijek; Strojarski fakultet u Slavonskom Brodu; Fakultet elektrotehnike, računarstva i informacijskih tehnologija Osijek; Građevinski i arhitektonski fakultet Osijek; Faculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in Osijek
• Subjects: Algorithms; Artificial neural networks; Blurring; Cross correlation; deep learning; Feature extraction; Machine learning; Methods; Neural networks; Occlusion; Optical tracking; Redundancy; siamese network; sparse transform; Tracking systems; visual tracking; China
• ISSN: 1330-3651; 1848-6339
• DOI: 10.17559/TV-20211115041517

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.