Review of recent advances in visual tracking techniques

Abstract

Visual tracking is the widely emerging research in computer vision applications. Nowadays, researchers have proposed various novel tracking methodologies to attain the excellence in terms of performance. In this review, several recent visual tracking methodologies have been clearly examined and categorised into four different categories such as Discriminative Trackers, Generative Trackers, Correlation Filter Based Trackers and Combined Trackers. Moreover, this study analyses and tabulates the methodologies applied in every recently proposed visual tracking method. The main objective of this review is to provide a detailed insight to the reader with the different aspects of tracking methodologies and future direction of tracking researches. The experimental evaluations on recent trackers have been documented for the better understanding of the performance of existing visual trackers on different benchmark datasets such as OTB 2015, VOT 2016 and MOT 2020.