This review provides a comprehensive overview of object tracking using computer vision, highlighting the development of methods, sensor equipment, datasets, and applications over the past decade. Object tracking is crucial for applications such as autonomous vehicles, robotics, and medical diagnostics. The review discusses various sensor types, including monocular cameras, depth-based cameras, and hybrid sensors, which are essential for accurate tracking. Monocular cameras are cost-effective but lack depth information, while depth-based cameras provide 3D data but may have limited range. Hybrid sensors combine multiple data sources to enhance tracking accuracy in complex environments. The review also examines different datasets used for benchmarking tracking methods, such as KITTI, HumanEVA, and OTB, which cover a wide range of scenarios. These datasets are vital for evaluating and improving tracking algorithms. The review emphasizes the importance of selecting appropriate sensors and datasets based on application requirements, considering factors such as cost, environment, and data availability. It also discusses the limitations of current methods and suggests future research directions, including the development of more diverse datasets and the integration of advanced sensor technologies. The review concludes that object tracking is a dynamic field with ongoing advancements, and further research is needed to address challenges in real-world applications.This review provides a comprehensive overview of object tracking using computer vision, highlighting the development of methods, sensor equipment, datasets, and applications over the past decade. Object tracking is crucial for applications such as autonomous vehicles, robotics, and medical diagnostics. The review discusses various sensor types, including monocular cameras, depth-based cameras, and hybrid sensors, which are essential for accurate tracking. Monocular cameras are cost-effective but lack depth information, while depth-based cameras provide 3D data but may have limited range. Hybrid sensors combine multiple data sources to enhance tracking accuracy in complex environments. The review also examines different datasets used for benchmarking tracking methods, such as KITTI, HumanEVA, and OTB, which cover a wide range of scenarios. These datasets are vital for evaluating and improving tracking algorithms. The review emphasizes the importance of selecting appropriate sensors and datasets based on application requirements, considering factors such as cost, environment, and data availability. It also discusses the limitations of current methods and suggests future research directions, including the development of more diverse datasets and the integration of advanced sensor technologies. The review concludes that object tracking is a dynamic field with ongoing advancements, and further research is needed to address challenges in real-world applications.