This paper presents a comprehensive benchmark for online object tracking, aiming to evaluate and analyze the performance of various tracking algorithms. The authors, Yi Wu, Jongwoo Lim, and Ming-Hsuan Yang, from the University of California at Merced and Hanyang University, review recent advances in online object tracking and conduct large-scale experiments using different evaluation criteria. They build a code library that includes 29 publicly available trackers and a test dataset with ground-truth annotations, covering a wide range of attributes such as occlusion, fast motion, and illumination variation. The evaluation methodology involves precision plots and success plots, as well as robustness evaluations through temporal and spatial perturbations of initial conditions. The results highlight the importance of background information, local models, and motion models in improving tracking performance. The paper also discusses the limitations of current trackers and suggests future research directions, emphasizing the need for more robust and adaptive tracking algorithms. The evaluation results provide a detailed understanding of the strengths and weaknesses of different tracking methods, contributing to the advancement of the field of online object tracking.This paper presents a comprehensive benchmark for online object tracking, aiming to evaluate and analyze the performance of various tracking algorithms. The authors, Yi Wu, Jongwoo Lim, and Ming-Hsuan Yang, from the University of California at Merced and Hanyang University, review recent advances in online object tracking and conduct large-scale experiments using different evaluation criteria. They build a code library that includes 29 publicly available trackers and a test dataset with ground-truth annotations, covering a wide range of attributes such as occlusion, fast motion, and illumination variation. The evaluation methodology involves precision plots and success plots, as well as robustness evaluations through temporal and spatial perturbations of initial conditions. The results highlight the importance of background information, local models, and motion models in improving tracking performance. The paper also discusses the limitations of current trackers and suggests future research directions, emphasizing the need for more robust and adaptive tracking algorithms. The evaluation results provide a detailed understanding of the strengths and weaknesses of different tracking methods, contributing to the advancement of the field of online object tracking.