计算机工程与应用2025，Vol.61Issue(4)：141-149,9.DOI:10.3778/j.issn.1002-8331.2309-0453
结合移动对象发现和对比学习的无监督跟踪
Unsupervised Tracking Combining Moving Object Discovery and Contrastive Learning
摘要
Abstract
The conventional deep learning tracking methods require numerous manually annotated video labels to com-plete supervised learning for object tracking tasks,while unsupervised tracking methods can train models by using unla-beled videos,which is beneficial for deployment in real-world scenarios.Traditional unsupervised tracking methods heavily rely on exploring the spatial information of training samples,so it is difficult to track the target objects which have intense movement changes over a long period of time.This paper proposes an unsupervised tracking method based on the theory of cyclic consistency,which adopts unsupervised optical flow estimation and dynamic programming to discover the moving targets.In addition,for the purpose of utilizing the rich temporal information,a cyclic memory learning scheme is used to construct a memory queue,and a method based on contrastive learning is proposed to update the template.The experi-mental results show that the proposed tracking method achieves EAO of 0.402 and 0.344 on the VOT2016 and VOT2018 datasets respectively.The main performance indicators are comparable to some mainstream supervised learning methods in terms of tracking performance.关键词
目标跟踪/无监督学习/循环一致性/移动对象发现/对比学习Key words
object tracking/unsupervised learning/cycle consistency/moving object discovery/contrastive learning分类
计算机与自动化引用本文复制引用
段苛苛,郑俊蓉,晏泽..结合移动对象发现和对比学习的无监督跟踪[J].计算机工程与应用,2025,61(4):141-149,9.基金项目
辽宁省科学技术计划面上项目(2023MS139). （2023MS139）
