SMSTracker: A Self-Calibration Multi-Head Self-Attention Transformer for Visual Object Tracking

Abstract

Visual object tracking plays a crucial role in computer vision. In recent years, researchers have proposed various methods to achieve high-performance object tracking. Among these, methods based on Transformers have become a research hotspot due to their ability to globally model and contextualize information. However, current Transformer-based object tracking methods still face challenges such as low tracking accuracy and the presence of redundant feature information. In this paper, we introduce self-calibration multi-head self-attention Transformer (SMSTracker) as a solution to these challenges. It employs a hybrid tensor decomposition self-organizing multi-head self-attention transformer mechanism, which not only compresses and accelerates Transformer operations but also significantly reduces redundant data, thereby enhancing the accuracy and efficiency of tracking. Additionally, we introduce a self-calibration attention fusion block to resolve common issues of attention ambiguities and inconsistencies found in traditional tracking methods, ensuring the stability and reliability of tracking performance across various scenarios. By integrating a hybrid tensor decomposition approach with a self-organizing multi-head self-attentive transformer mechanism, SMSTracker enhances the efficiency and accuracy of the tracking process. Experimental results show that SMSTracker achieves competitive performance in visual object tracking, promising more robust and efficient tracking systems, demonstrating its potential to provide more robust and efficient tracking solutions in real-world applications.