Transformer Object Tracking Algorithm Based on Missile-borne Image
The classic object tracking method based on missile borne image self seeking relies on traditional feature extraction methods,which usually can only extract limited features and have insufficient representation ability,and the algorithms are sus-ceptible to tracking failures due to the interference of factors such as changes in target scales,similar targets,and complex backgrounds in missile-borne images.Since Transformer is widely used in the field of object tracking by virtue of its powerful global modeling capability.In this paper,the Transformer object tracking algorithm based on missile-borne image is proposed by combining the experimental platform of missile-borne image simulation,which consists of three parts:feature extraction,fea-ture fusion and prediction head.First,deep features are extracted from the input initial template and the search region using the first three layers of the Swin-Transformer network,respectively,in the feature extraction part.Second,in order to fully utilize the initial template information,the extracted features are feature enhanced with the help of the cross-attention module.Then,the extracted features are spliced and fed to the encoder and decoder modules for fusion of features.Finally,the output features are regressed and classified header for target localization.The algorithm is experimented on the missile-borne image dataset,and the tracking success rate reaches73.87%,and the tracking speed reaches56.79 frames/s.Compared to the classic KCF algorithm,the algorithm in this article fully utilizes the characteristics of the Transformer attention mechanism to improve the tracking success rate and accuracy by 18.01%and23.14%,significantly enhancing the robustness of the algorithm.
万方数据
维普
