This review article provides a comprehensive overview of object tracking in computer vision, focusing on advancements in hardware, datasets, image processing, and deep learning methods. The authors highlight the importance of object tracking in various applications such as robotics, autonomous driving, and pedestrian movement. They discuss the evolution of object tracking methods over the past decade, emphasizing the integration of different sensors and the development of image processing and deep learning algorithms. The review covers different types of sensor equipment, including monocular cameras, depth-based cameras, and hybrid sensors, and their applications in object tracking. It also discusses various datasets used for benchmarking object tracking methods, such as KITTI, VOT, and MOT, and provides guidelines for selecting appropriate sensors and datasets based on specific application requirements. Additionally, the article reviews different approaches and methods for object tracking, including classical image processing techniques and deep learning models. It highlights the strengths and limitations of each approach and provides a taxonomy of the methods used in object tracking. The review concludes with research questions and future directions to address unresolved issues in the field of object tracking.This review article provides a comprehensive overview of object tracking in computer vision, focusing on advancements in hardware, datasets, image processing, and deep learning methods. The authors highlight the importance of object tracking in various applications such as robotics, autonomous driving, and pedestrian movement. They discuss the evolution of object tracking methods over the past decade, emphasizing the integration of different sensors and the development of image processing and deep learning algorithms. The review covers different types of sensor equipment, including monocular cameras, depth-based cameras, and hybrid sensors, and their applications in object tracking. It also discusses various datasets used for benchmarking object tracking methods, such as KITTI, VOT, and MOT, and provides guidelines for selecting appropriate sensors and datasets based on specific application requirements. Additionally, the article reviews different approaches and methods for object tracking, including classical image processing techniques and deep learning models. It highlights the strengths and limitations of each approach and provides a taxonomy of the methods used in object tracking. The review concludes with research questions and future directions to address unresolved issues in the field of object tracking.