Object Tracking Algorithm Based on Dual-Model Competition Mechanism

LIN Bin; WANG Hua-tong; FENG Quan-xi

doi:10.12263/DZXB.20221375

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Object Tracking Algorithm Based on Dual-Model Competition Mechanism

PAPERS | 更新时间：2025-12-08

- Object Tracking Algorithm Based on Dual-Model Competition Mechanism
- ACTA ELECTRONICA SINICA Vol. 51, Issue 5, Pages: 1381-1387(2023)
- 作者机构：
  
  1.桂林理工大学理学院,广西桂林 541004
  2.广西高校应用统计重点实验室,广西桂林 541004
  3.广东外语外贸大学信息科学与技术学院,广东广州 510006
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China(62166015);Guangxi Natural Science Foundation(2019GXNSFBA245056)
- DOI：10.12263/DZXB.20221375
  CLC： TP391.4
- Received：07 December 2022，
  
  Revised：2023-02-19，
  
  Published：25 May 2023
- 稿件说明：
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林彬,王华通,封全喜.基于双模型竞争机制的目标跟踪算法[J].电子学报,2023,51(05):1381-1387.

LIN Bin,WANG Hua-tong,FENG Quan-xi.Object Tracking Algorithm Based on Dual-Model Competition Mechanism[J].ACTA ELECTRONICA SINICA,2023,51(05):1381-1387.
林彬,王华通,封全喜.基于双模型竞争机制的目标跟踪算法[J].电子学报,2023,51(05):1381-1387. DOI： 10.12263/DZXB.20221375.

LIN Bin,WANG Hua-tong,FENG Quan-xi.Object Tracking Algorithm Based on Dual-Model Competition Mechanism[J].ACTA ELECTRONICA SINICA,2023,51(05):1381-1387. DOI： 10.12263/DZXB.20221375.

摘要

为解决背景感知相关滤波器存在的特征表达能力不足和模型漂移问题，本文提出了一种基于双模型竞争机制的目标跟踪算法.一方面，本文基于颜色和梯度信息设计了一种简单高效的特征描述子，以实现更鲁棒的目标表观建模.另一方面，本文分别构建初始模型和变化模型作用于目标搜索区域，并根据两者的跟踪响应图置信度来决定跟踪结果.跟踪过程中，随着双模型主导地位不断地动态切换，变化模型也被赋予了可逆向学习的能力，从而达到缓解模型漂移的效果.实验结果表明，相比于基准算法，本文算法在OTB2015、TinyTLP和UAV20L三个数据集的跟踪精度分别提升5.0%、1.3%和4.1%，跟踪成功率分别提升3.8%、2.8%和1.7%，且在对不同跟踪场景实现稳定跟踪的同时能够保持25.5 fps的实时跟踪速度.

Abstract

To solve the problems of insufficient feature expression ability and model drift in the background-aware correlation filters

this paper proposes an object tracking algorithm based on a dual-model competition mechanism.On the one hand

a simple and efficient feature descriptor that integrates color and gradient information is designed to achieve more robust target appearance modeling.On the other hand

we construct two filter models to describe the object's initial appearance and its variations

and then apply them to the target searching area respectively.The tracking results are determined by the confidence of the tracking response maps corresponding to these two models.During the tracking process

with the dynamic switching of the dominant position of the two models

the filter model for adapting to object variations is also endowed with the ability of reversible learning to alleviate the model drift.The experimental results show that

compared with the baseline tracker

the tracking precision of the proposed algorithm on OTB2015

TinyTLP and UAV20L datasets is improved by 5.0%

1.3% and 4.1%

and the tracking success rate is improved by 3.8%

2.8% and 1.7%.The proposed algorithm can also achieve stable tracking performance for different tracking scenarios while maintaining a running speed of 25.5 fps.

关键词

Keywords

references

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