基于Transformer的单目标跟踪研究进展

doi:10.16451/j.cnki.issn1003-6059.202604004

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摘要

单目标跟踪（Single Object Tracking, SOT）是计算机视觉领域的核心任务之一.然而,在复杂场景中,传统的相关滤波和孪生网络架构已难以满足更高的精度与鲁棒性要求.近年来,Transformer架构凭借其卓越的全局建模能力,在SOT中表现出显著优势.因此,文中系统梳理基于Transformer的SOT研究进展.依据整体结构的设计,将现有算法分为双流双阶段跟踪算法与单流单阶段跟踪算法,并深入归纳分析其中具有代表性的跟踪算法,同时总结轻量化Transformer跟踪方法的研究现状.此外,进一步探讨基于Mamba的跟踪方法和通用模型等前沿热点,它们在模型效率与通用性方面展现出良好潜力.在多个主流数据集上对不同Transformer跟踪算法的性能进行全面评估和对比分析.最后,展望SOT在模型轻量化、多模态融合、长时跟踪、大模型驱动跟踪等方面的研究趋势,期望为单目标跟踪的研究与发展提供参考.

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	张大伟
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关键词 ：计算机视觉, Transformer, 双流双阶段跟踪算法, 单流单阶段跟踪算法

Abstract：

Single object tracking（SOT） is recognized as one of the fundamental tasks in the field of computer vision. However, traditional correlation filters and Siamese network architectures struggle to meet the growing demands for accuracy and robustness in complex and dynamic environments. Transformer exhibits significant advantages in SOT by virtue of its powerful global modeling capability. Therefore, the recent research advances in Transformer-based SOT are reviewed systematically. Based on the overall pipeline design, existing tracking algorithms can be categorized into two primary types： two-stream two-stage algorithms and one-stream one-stage algorithms. Representative algorithms of each category are analyzed in depth to highlight their relations and characteristics, while the research status of lightweight Transformer-based tracking methods is summarized. In addition, recent emerging trends, such as Mamba-based tracking algorithms and unified model architectures, are further investigated and their promising potential in model efficiency and generalizability is discussed as well. The performance of different Transformer-based tracking methods is comprehensively analyzed and evaluated on multiple mainstream datasets. Finally, several promising directions for future research in SOT, including lightweight models, multimodal fusion, long-term tracking and foundation models-driven tracking, are outlined, providing valuable references for the research and development of SOT.

Key words： Computer Vision Transformer Two-Stream Two-Stage Tracking Algorithm One-Stream One-Stage Tracking Algorithm

收稿日期: 2025-12-22

ZTFLH:

TP391.41

基金资助:

国家自然科学基金项目（No.62402449,62272419）、浙江省自然科学基金重大项目（No.LD26F020003）、多模态认知计算安徽省重点实验室（安徽大学）开放基金项目（No.MMC202409）资助

通讯作者: 郑忠龙,博士,教授,主要研究方向为模式识别、机器学习、图像处理等.E-mail：zhonglong@zjnu.edu.cn.

作者简介: 张大伟,博士,副教授,主要研究方向为深度学习、计算机视觉、模式识别等.E-mail：davidzhang@zjnu.edu.cn. 徐东升,硕士研究生,主要研究方向为计算机视觉、目标跟踪等.E-mail：dongshengxu@zjnu.edu.cn. 余哲晨,博士研究生,主要研究方向为人工智能、计算机视觉等.E-mail：yzc1201@zjnu.edu.cn. 蒋凯薇,硕士研究生,主要研究方向为深度学习、计算机视觉等.E-mail：viviaaannn@zjnu.edu.cn. 田卫刚,硕士研究生,主要研究方向为计算机视觉、目标跟踪等.E-mail：weigangtian@zjnu.edu.cn.

引用本文:

张大伟, 徐东升, 余哲晨, 蒋凯薇, 田卫刚, 郑忠龙. 基于Transformer的单目标跟踪研究进展[J]. 模式识别与人工智能, 2026, 39(4): 348-378. ZHANG Dawei, XU Dongsheng, YU Zhechen, JIANG Kaiwei, TIAN Weigang, ZHENG Zhonglong. Research Progress of Single Object Tracking Based on Transformer. Pattern Recognition and Artificial Intelligence, 2026, 39(4): 348-378.

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