Anchor free based Siamese network tracker with transformer for RGB-T tracking

Abstract

Abstract In recent years, many RGB-THERMAL tracking methods have been proposed to meet the needs of single object tracking under different conditions. However, these trackers are based on ANCHOR-BASED algorithms and feature cross-correlation operations, making it difficult to improve the success rate of target tracking. We propose a siamAFTS tracking network, which is based on ANCHOR-FREE and utilizes a fully convolutional training network with a Transformer module, suitable for RGB-THERMAL target tracking. This model addresses the issue of low success rate in current mainstream algorithms. We also incorporate channel and channel spatial attention modules into the network to reduce background interference on predicted bounding boxes. Unlike current ANCHOR-BASED trackers such as MANET, DAPNet, SGT, and ADNet, the proposed framework eliminates the use of anchor points, avoiding the challenges of anchor hyperparameter tuning and reducing human intervention. Through repeated experiments on three datasets, we ultimately demonstrate the improved success rate of target tracking achieved by our proposed tracking network.