Discriminative learning of online appearance modeling methods for visual tracking

Appearance variations are a challenging issue in visual tracking systems. Typically, appearance modeling is used to deal with the challenge of representing and detecting objects in these systems. Appearance modeling is generally structured of parts such as visual target representation and online learning update modeling. Various online learning methods have been proposed to perform the task of object representation and update the model. The discriminative online learning model, as the main focus of the study, is investigated in this paper. Correspondingly, describe current procedures fully, highlighting their benefits and drawbacks. This study aims to give in-depth research into methodologies based on discriminative online learning. A critical review of current approaches' benefits and drawbacks is covered. The finding of this research is investigation of discriminative online learning methods for appearance modeling in visual tracking systems. It provides a comprehensive analysis of current approaches, evaluating their benefits and drawbacks, and comparing their performance to identify the most effective approach for addressing appearance variations in object tracking. The approaches are evaluated, and performance comparisons are made to identify the most effective approach to discriminative online learning for appearance modeling.