A Survey on Visual Object Tracking: Datasets, Methods and Metrics

---------------------------------------------------------------------***--------------------------------------------------------------------Abstract: Object detection and tracking is an important and challenging task in many critical computer vision applications such as automated video surveillance, traffic monitoring, autonomous robot navigation, and smart environments. Object tracking can be defined as the process of segmenting an object of interest from a video scene and keeping track of its motion, orientation and occlusion to extract useful information. Several object tracking methods have been proposed in the past two decades aiming to design a robust object tracker addressing all the practical challenges in the real work. The goal of this paper is to review the datasets, methods, and metrics available in the literature for developing a robust visual object tracker. Particularly, we present the details about five publicly available datasets, twenty object tracking methods, and three metrics for comparing the performance of the visual object tracking systems.