Visual object tracking: A survey

Visual object tracking is an important area in computer vision, and many tracking algorithms have been proposed with promising results. Existing object tracking approaches can be categorized into generative trackers, discriminative trackers, and collaborative trackers. Recently, object tracking algorithms based on deep neural networks have emerged and obtained great attention from researchers due to their outstanding tracking performance. To summarize the development of object tracking, a few surveys give analyses on either deep or non-deep trackers. In this paper, we provide a comprehensive overview of state-of-the-art tracking frameworks including both deep and non-deep trackers. We present both quantitative and qualitative tracking results of various trackers on five benchmark datasets and conduct a comparative analysis of their results. We further discuss challenging circumstances such as occlusion, illumination, deformation, and motion blur. Finally, we list the challenges and the future work in this fast-growing field.