计算机与数字工程2025，Vol.53Issue(3)：725-733,9.DOI:10.3969/j.issn.1672-9722.2025.03.020
利用自适应频域滤波和凝聚损失的目标跟踪
Object Tracking Via Adaptive Frequency Domain Filter and Condensation Loss
摘要
Abstract
Visual object tracking(VOT)is a fundamental task in computer vision,which has widely applied in realistic sce-narios.Therefore,the research on visual object tracking is of great significance.There are three unsolved problems in previous VOT algorithms.Firstly,with the proposal of ResNet,spatial feature extraction is greatly strengthened,but there inherently have several drawbacks.Secondly,for the extracted features,how to leverage useful information and suppress useless information is still chal-lenging for VOT.Thirdly,the sample imbalance leads to the problem that the model lacks the discriminative ability,which limits the performance of the learned model.In order to solve the issues above,this paper uses the dual feature enhancement module to en-hance the spatial features extracted by ResNet.The spatial domain features are transformed into frequency domain features by the Fourier transform,and then the suitable features for the tracker are automatically highlighted by employing the learnable filter in the frequency domain,while suppressing other chaotic background information.Finally,the aggregation loss is introduced to expand the range of difficult samples and punish simple samples,which significantly alleviates the problem of sample imbalance.A large number of experimental results on four challenging datasets show that the proposed method has favorable performance against SOTA algorithms.In particular,the proposed approach achieves a AUC score of 62.0%in the TLP and 1.7%improvement in the VOT2020LT.关键词
目标跟踪/双重特征增强模块/自适应频域滤波器/样本不平衡/凝聚损失Key words
object tracking/dual feature enhancement module/adaptive frequency domain filter/sample imbalance/coagu-lation loss分类
信息技术与安全科学引用本文复制引用
孙培盛,樊佳庆,宋慧慧..利用自适应频域滤波和凝聚损失的目标跟踪[J].计算机与数字工程,2025,53(3):725-733,9.基金项目
国家自然科学基金项目(编号:61532009) （编号:61532009）
江苏省自然科学基金项目(编号:BK20191397)资助. （编号:BK20191397）
