Object Tracking Methods:A Review

Abstract

Object tracking is one of the most important tasks in computer vision that has many practical applications such as traffic monitoring, robotics, autonomous vehicle tracking, and so on. Different researches have been done in recent years, but because of different challenges such as occlusion, illumination variations, fast motion, etc. researches in this area continues. In this paper, various methods of tracking objects are examined and a comprehensive classification is presented that classified tracking methods into four main categories of feature-based, segmentation-based, estimation-based, and learning-based methods that each of which has its own sub-categories. The main focus of this paper is on learning-based methods, which are classified into three categories of generative methods, discriminative methods, and reinforcement learning. One of the sub-categories of the discriminative model is deep learning. Because of high-performance, deep learning has recently been very much considered.