论文标题
单一对象跟踪：方法，数据集和评估指标的调查
Single Object Tracking: A Survey of Methods, Datasets, and Evaluation Metrics
论文作者
论文摘要
对象跟踪是计算机视觉中最重要的任务之一，该任务具有许多常合价应用程序，例如交通监控，机器人技术，自动驾驶汽车跟踪等。稍后已经尝试了不同的研究，但是由于诸如遮挡，照明变化，快速运动等各种挑战之类的研究继续进行。在本文中，检查了以下对象的不同策略，并显示了全面的分类，将以下策略分为四个基于特征的，基于细分，基于估计的基于估计和基于学习的方法的四个基本类别，每种方法都具有其主张子类别。本文中最重要的是基于学习的策略，这些策略分为三类生成策略，歧视性策略和强化学习。歧视性节目的子类别之一是深度学习。自从高性能以来，深度学习一直被广泛考虑。最后，将引入最常用的不同数据集和评估方法。
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.
