A review of object tracking based on deep learning

The rapid advancement of deep learning has led to a surge in the development of object-tracking algorithms. Given the diverse objectives, backbone networks, and application methodologies, this study aims to integrate the prevalent tracking approaches comprehensively. We propose a systematic classification scheme based on application scenarios and primary methods, accompanied by a thorough analysis and concise summaries of each category. This approach provides a broader coverage of tracking techniques, facilitating a quicker understanding of the domain for novice researchers. In addition, we present standardized evaluation metrics and widely used datasets, including cross-method performance comparisons of selected algorithms on identical benchmarks to enhance the reader’s contextual understanding. Finally, we offer a critical assessment of current limitations, practical recommendations, and forward-looking perspectives to guide future research directions.