Single object tracking is a well-known and challenging research topic in computer vision. Over the last two decades, numerous researchers have proposed various algorithms to solve this problem and achieved promising results. Recently, Transformer-based tracking approaches have ushered in a new era in single object tracking due to their superior tracking robustness. Although several survey studies have been conducted to analyze the performance of trackers, there is a need for another survey study after the introduction of Transformers in single object tracking. In this survey, we aim to analyze the literature and performances of Transformer tracking approaches. Therefore, we conduct an in-depth literature analysis of Transformer tracking approaches and evaluate their tracking robustness and computational efficiency on challenging benchmark datasets. In addition, we have measured their performances on different tracking scenarios to find their strength and weaknesses. Our survey provides insights into the underlying principles of Transformer tracking approaches, the challenges they face, and their future directions.

翻译：单目标跟踪是计算机视觉中一项众所周知且具有挑战性的研究课题。过去二十年间，众多研究者提出了各种算法来解决这一问题，并取得了令人瞩目的成果。近年来，基于Transformer的跟踪方法凭借其卓越的跟踪鲁棒性，开创了单目标跟踪的新纪元。尽管已有若干综述研究分析了跟踪器的性能，但在Transformer引入单目标跟踪领域后，仍需开展另一项综述研究。本综述旨在分析Transformer跟踪方法的相关文献及其性能表现。为此，我们深入剖析了Transformer跟踪方法的文献，并在具有挑战性的基准数据集上评估了其跟踪鲁棒性和计算效率。此外，我们还测量了它们在不同跟踪场景下的性能，以发现其优势与不足。本综述为理解Transformer跟踪方法的基本原理、面临的挑战以及未来发展方向提供了深刻见解。