Lightweight Siamese Network for Visual Tracking via FasterNet and Feature Adaptive Fusion

Visual target tracking on the strength of Siamese network have made encouraging progress over recent years, providing significant advantages in the aspect of real-time and accuracy performance. Nevertheless, most of these trackers are ResN et-driver trackers with a host of parameters and high requirements for hardware. This leads to be inapplicable in some certain scenarios. In this paper, we present a lightweight but accurate tracker named SiamFaster. Using lightweight FasterNet constructed by partial convolution instead of ResN et backbone. The feature adaptive fusion module is added as a strategy that can better integration of multi-layers features and achieve better tracking performance. The new tracker is evaluated on the challenging benchmarks of OTB100 and GOT-10K and compare with some high-performance tracker. The results show that our tracker performs relatively well, with algorithm speeds up to an impressive 185 FPS.