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

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