Single Object Tracking Algorithm Based on Preceding-Feature Prompts

ZHENG Ying; MA Guoliang; GUO Jian; WANG Ziyi

doi:10.12382/bgxb.2025.0829

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Single Object Tracking Algorithm Based on Preceding-Feature Prompts

更新时间：2026-04-23

- Single Object Tracking Algorithm Based on Preceding-Feature Prompts
- Acta Armamentarii (2026)
- 作者机构：
  
  1. 南京理工大学自动化学院,江苏,南京,210094
  2. 南京理工大学能源与动力工程学院,江苏,南京,210094
- 作者简介：
- 基金信息：
- DOI：10.12382/bgxb.2025.0829
  CLC： TP391
- Received：09 September 2025，
  
  Online First：23 April 2026，
- 稿件说明：
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郑颖，马国梁，郭健，等. 基于前序特征提示的单目标跟踪算法[J/OL]. 兵工学报, 2026(2026-04-23). https://doi.org/10.12382/bgxb.2025.0829.

ZHENG Y, MA G L, GUO J, et al. Single object tracking algorithm based on preceding-feature prompts[J/OL]. Acta Armamentarii, 2026(2026-04-23). https://doi.org/10.12382/bgxb.2025.0829. (in Chinese)
郑颖，马国梁，郭健，等. 基于前序特征提示的单目标跟踪算法[J/OL]. 兵工学报, 2026(2026-04-23). https://doi.org/10.12382/bgxb.2025.0829. DOI：

ZHENG Y, MA G L, GUO J, et al. Single object tracking algorithm based on preceding-feature prompts[J/OL]. Acta Armamentarii, 2026(2026-04-23). https://doi.org/10.12382/bgxb.2025.0829. (in Chinese) DOI：

摘要

现有单目标跟踪算法普遍依赖初始帧模板与当前帧搜索区域之间的相似性度量进行匹配，在复杂场景中易出现跟踪漂移和误匹配问题，为此提出一种基于前序特征提示的Transformer目标跟踪算法。通过广义关系建模模块对搜索区域内的目标与背景令牌进行自适应划分，仅允许目标令牌与模板令牌交互以有效抑制相似目标及环境噪声干扰；在Transformer目标跟踪算法框架中新增加了前序特征提示模块，结合令牌划分结果与当前帧预测结果生成精确的目标蒙版，并利用前序特征提示编码器对蒙版与搜索图像的拼接结果进行联合编码；设计的前序特征提示解码器根据当前帧搜索区域特征自适应聚合编码结果生成特定提示，从而提升了跟踪算法的适应能力。采用多个基准数据集进行测试。研究结果表明：所提算法在GOT-10K数据集上平均重叠率达到76.1%，在LaSOT数据集上成功率曲线下面积为72.3%、精准度为82.4%；所提算法跟踪性能较优，能够有效解决单目标跟踪时的遮挡、形变和相似干扰等问题。

Abstract

Most existing single-object tracking algorithms rely on similarity matching between the initial-frame template and the search region of the current frame. However

in complex scenarios

this often leads to tracking drift and mismatches. To address these issues

we propose a Transformer-based tracking algorithm enhanced with preceding-feature prompts. A generalized relation modeling module adaptively partitions target and background tokens within the search region

allowing only target tokens to interact with template tokens. This effectively suppresses interference from similar objects and environmental noise. Within the Transformer tracking framework

we introduce apreceding-featureprompt module that combines token partitioning results with the current prediction to generate precise target masks. These masks are jointly encoded with the search image by a prior feature prompt encoder. A corresponding decoder then adaptively aggregates the encoded information with search-region features to produce task-specific prompts

thereby improving adaptability. Evaluations on multiple benchmark datasets show that our method achieves an average overlap of 76.1% on GOT-10Kdataset

an area-under-curve of 72.3%

and a precision of 82.4% on LaSOTdataset. The results demonstrate superior tracking performance

particularly in handling occlusion

deformation

and interference from similar objects.

关键词

Keywords

references

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