Visual Object Tracking Algorithm Based on Adaptive Feature Selection

WANG Cai-xia; AN Qi; ZHOU Hong-ce; LI Yi-peng

doi:10.12263/DZXB.20250046

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Visual Object Tracking Algorithm Based on Adaptive Feature Selection

PAPERS | 更新时间：2025-12-27

- Visual Object Tracking Algorithm Based on Adaptive Feature Selection
- ACTA ELECTRONICA SINICA Vol. 53, Issue 8, Pages: 2879-2898(2025)
- 作者机构：
  
  1.长春理工大学电子信息工程学院，吉林长春 130022
  2.吉林省光电检测与智能信息处理工程技术研究中心，吉林长春 130022
- 作者简介：
- 基金信息：
  
  National Key Research and Development Program of China(2018YFB1107600);Jilin Provincial Department of Science and Technology Project(20210201021GX)
- DOI：10.12263/DZXB.20250046
  CLC： TP391;
- Received：11 January 2025，
  
  Accepted：10 June 2025，
  
  Published：25 August 2025
- 稿件说明：
移动端阅览
王彩霞, 安琪, 周鸿策, 等. 基于特征自适应选取的视觉目标跟踪算法[J]. 电子学报, 2025, 53(08): 2879-2898.

WANG Cai-xia, AN Qi, ZHOU Hong-ce, et al. Visual Object Tracking Algorithm Based on Adaptive Feature Selection[J]. Acta Electronica Sinica, 2025, 53(08): 2879-2898.
王彩霞, 安琪, 周鸿策, 等. 基于特征自适应选取的视觉目标跟踪算法[J]. 电子学报, 2025, 53(08): 2879-2898. DOI：10.12263/DZXB.20250046

WANG Cai-xia, AN Qi, ZHOU Hong-ce, et al. Visual Object Tracking Algorithm Based on Adaptive Feature Selection[J]. Acta Electronica Sinica, 2025, 53(08): 2879-2898. DOI：10.12263/DZXB.20250046

摘要

针对现有视觉目标跟踪算法始终选择所有的历史模板与全部搜索区域进行交互，导致有强背景干扰或者目标发生形变时产生的跟踪失败问题，提出一种基于特征自适应选取的视觉目标跟踪算法.首先，通过模板特征过滤器将传统图像级模板更新优化为特征级动态更新，筛选当前帧的强相关模板特征并压缩弱相关特征，减少冗余信息干扰；其次，采用搜索特征鉴别器自适应划分搜索区域中潜在的目标特征与噪声特征，抑制无关区域的交互；最后，引入时空信息传播令牌，跨帧传递目标外观与位置信息，逐帧修正跟踪响应；设计基于分离注意力机制的特征交互编码器，将自注意力与交叉注意力分离，适配上述模块并增强判别能力.在多种大规模公开数据集上的实验取得了鲁棒结果，在OTB100、LaSOT和UAV123数据集上的精度分别达到93.0%、79.6%和91.2%，且算法能够实现跟踪成功率与跟踪速度的良好平衡，提升了跟踪器在复杂场景下的准确性和鲁棒性.

Abstract

To address the persistent tracking failures caused by strong background interference or target deformation in existing visual object tracking algorithms that indiscriminately utilize all historical templates and interact with entire search regions

this paper proposes a feature-adaptive selection based visual object tracking algorithm. First

a template feature filter is introduced to optimize traditional image-level template updating into feature-level dynamic updating

which selectively preserves strongly correlated template features while compressing weakly relevant features to reduce redundant information interference. Second

a search feature discriminator is employed to autonomously distinguish potential target features from noise features in search regions

thereby suppressing interactions with irrelevant areas. Furthermore

spatio-temporal information propagation tokens are incorporated to transmit target appearance and positional information across frames for progressive response refinement. A feature interaction encoder based on decoupled attention mechanisms is designed

which separates self-attention and cross-attention operations to better adapt to the proposed modules while enhancing discriminative capabilities. Comprehensive experiments on multiple large-scale public datasets demonstrate robust performance

achieving precision scores of 93.0%

79.6%

and 91.2% on OTB100

LaSOT

and UAV123 datasets respectively. The algorithm maintains an optimal balance between tracking success rate and operational efficiency

significantly improving tracking accuracy and robustness in complex scenarios.

关键词

Keywords

references

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