Correlation Filtering Tracking Algorithm Based on Adaptive Aspect-Ratio

ZHONG Yu-bin; YANG Peng; DOU Lei

doi:10.12263/DZXB.20230162

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Correlation Filtering Tracking Algorithm Based on Adaptive Aspect-Ratio

PAPERS | 更新时间：2025-12-11

- Correlation Filtering Tracking Algorithm Based on Adaptive Aspect-Ratio
- ACTA ELECTRONICA SINICA Vol. 52, Issue 6, Pages: 2112-2122(2024)
- 作者机构：
  
  南京理工大学瞬态物理国家重点实验室，江苏南京 210094
- 作者简介：
- 基金信息：
  
  the National Science Foundation of China(60904085);Foundation of National Key Laboratory of Transient Physics
- DOI：10.12263/DZXB.20230162
  CLC： TP319.41;
- Received：24 February 2023，
  
  Revised：2023-11-09，
  
  Published：25 June 2024
- 稿件说明：
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钟钰彬, 杨鹏, 窦磊. 基于纵横比自适应的相关滤波跟踪算法[J]. 电子学报, 2024, 52(06): 2112-2122.

ZHONG Yu-bin, YANG Peng, DOU Lei. Correlation Filtering Tracking Algorithm Based on Adaptive Aspect-Ratio[J]. Acta Electronica Sinica, 2024, 52(06): 2112-2122.
钟钰彬, 杨鹏, 窦磊. 基于纵横比自适应的相关滤波跟踪算法[J]. 电子学报, 2024, 52(06): 2112-2122. DOI：10.12263/DZXB.20230162

ZHONG Yu-bin, YANG Peng, DOU Lei. Correlation Filtering Tracking Algorithm Based on Adaptive Aspect-Ratio[J]. Acta Electronica Sinica, 2024, 52(06): 2112-2122. DOI：10.12263/DZXB.20230162

摘要

由于跟踪过程目标不规则形变的影响，采用固定纵横比的尺度模型无法精确地估计目标的尺度.为解决该问题，本文提出基于纵横比自适应的相关滤波跟踪算法.基于fDSST（fast Discriminative Scale Space Tracking）算法，训练学习纵横比模型，更新目标的纵横比，获取更精确的目标尺度.在此基础上，本文设计了平滑修正方案以及学习率自适应机制，可以有效地缓解因目标出现遮挡导致的模型漂移问题.在OTB100、VOT2016和VOT2018数据集上与其他跟踪算法进行对比实验，结果表明本文算法改善了基准算法的性能，特别是在OTB100上的总体准确率和成功率比fDSST提高了9.6%和6.2%.

Abstract

Due to the irregular deformation of target in the tracking process

it is unable to accurately estimate the target scale

while using the scale model with fixed aspect ratio. In this paper

we propose an aspect-ratio-based correlation filtering tracking algorithm to address this problem. Based on the fDSST (fast Discriminative Scale Space Tracking) algorithm

first train and learn an aspect-ration model to update the aspect ratio of the target

which could help to obtain a more accurate target scale. On this basis

this paper designs a smoothing correction scheme and an adaptive learning rate mechanism to alleviate the model drift and achieve more accurate tracking. The results of comparative experiments on OTB100

VOT2016 and VOT2018 datasets show that the proposed algorithm improves the performance of the baseline algorithm. Especially

the overall precision and success rate of the proposed algorithm on OTB100 are 9.6% and 6.2% higher than those of fDSST.

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references

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