Generative online learning of appearance modeling approaches for visual tracking.

Appearance modeling plays a pivotal role in both object detection and visual tracking applications, making it a focal point for researchers due to its profound impact on real-world scenarios. It typically comprises components such as visual target representation and online learning update modeling. In this study, we embark on a comprehensive examination of generative online learning models, providing an in-depth review of existing methods with a detailed exploration of their strengths and weaknesses. The overarching objective of this research is to offer an extensive analysis of approaches grounded in generative online learning. Furthermore, we conduct a critical assessment that not only highlights the pros and cons of established techniques but also culminates in an evaluation and performance comparison to discern the most effective generative online learning method for the task of appearance modeling. [ABSTRACT FROM AUTHOR]