Single Object Tracking: A Survey of Methods, Datasets, and Evaluation Metrics

摘要

Object tracking is one of the foremost assignments in computer vision that has numerous commonsense applications such as traffic monitoring, robotics, autonomous vehicle tracking, and so on. Different researches have been tried later a long time, but since of diverse challenges such as occlusion, illumination variations, fast motion, etc. researches in this area continues. In this paper, different strategies of the following objects are inspected and a comprehensive classification is displayed that classified the following strategies into four fundamental categories of feature-based, segmentation-based, estimation-based, and learning-based methods that each of which has its claim sub-categories. The most center of this paper is on learning-based strategies, which are classified into three categories of generative strategies, discriminative strategies, and reinforcement learning. One of the sub-categories of the discriminative show is deep learning. Since of high-performance, deep learning has as of late been exceptionally much consider. Finally, the different datasets and the evaluation methods that are most commonly used will be introduced.