An Anchor-Free Siamese Tracker with Multi-Attention and Corner Detection Mechanism

Abstract

In recent times, anchor-based visual object trackers have become increasingly popular due to their exceptional performance. However, they rely on preset anchor boxes that require manual tuning, which can impact the performance of the trackers and introduce hyper-parameter dependencies. To address these issues, an anchor-free Siamese tracker with multi-attention and corner detection mechanism was proposed. Additionally, a multiple attention fusion module was created to calculate the relationship between the template and the search area in different channels, thus enhancing the model’s perception of environmental information. By eliminating the need for anchor points and performing direct computation, the proposed model minimizes the influence of hyper-parameters and human factors, resulting in improved overall efficiency. To showcase the effectiveness of the proposed tracker, comprehensive experiments were conducted on four challenging benchmarks, including OTB100, VOT2016, UAV123, and GOT-10k.