液晶与显示2025，Vol.40Issue(8)：1202-1218,17.DOI:10.37188/CJLCD.2025-0081
基于深度学习的单目标跟踪算法研究进展
Review of single object tracking algorithm based on deep learning
摘要
Abstract
Single object tracking is a crucial task in computer vision,aiming to accurately locate a target in a video sequence.Although deep learning has significantly advanced the field of single object tracking,challenges such as target deformation,complex backgrounds,occlusion,and scale variation still remain.This paper systematically reviews the development of deep learning-based single object tracking methods over the past decade,covering traditional sequence models based on convolutional neural networks,recurrent neural networks,and Siamese networks,as well as hybrid architectures combining convolutional neural networks with Transformers and the latest approaches entirely based on Transformers.Furthermore,we evaluate the performance of different methods in terms of accuracy,robustness,and computational efficiency on benchmark datasets such as OTB100,LaSOT,and GOT-10k,followed by an in-depth analysis.Finally,we discuss the future research directions of deep learning-based single object tracking algorithms.关键词
单目标跟踪/视觉目标跟踪/深度学习/卷积神经网络Key words
single-object tracking/deep learning/visual object tracking/Transformer tracking分类
信息技术与安全科学引用本文复制引用
高世严,柳杰,陈文艺,贺泽民,杨海燕,苗宗成..基于深度学习的单目标跟踪算法研究进展[J].液晶与显示,2025,40(8):1202-1218,17.基金项目
国家自然科学基金(No.52173263) （No.52173263）
国家重点研发计划(No.2022YFB3603703)Supported by National Natural Science Foundation of China(No.52173263) （No.2022YFB3603703）
National Key Research and Development Program of China(No.2022YFB3603703) （No.2022YFB3603703）
