基于深度学习的单目标跟踪算法研究进展

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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 stillremain. 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 diferent methods in terms of accuracy，robustness，and computational efficiency on benchmark datasets such as OTBlOO，LaSOT，and GOT- ⋅10k ，followed by an in-depth analysis. Finally，we discussthe future research directions of deep learning-based single object tracking algorithms. Key words： single-object tracking; deep learning;visual object tracking； Transformer tracking

1引言

计算机视觉的核心目标之一是赋予机器类人视觉的基础功能。（剩余34373字）