Target Tracking Based on Radon Transform Data Appearance Modeling

This article mainly focuses on an important challenge in target tracking in complex environments the real-time performance of algorithm operation. A new target appearance model based on Radon transform data is studied, and it is introduced into the correlation filtering framework for filtering template training. A fast tracking algorithm and target scale update scheme based on correlation filtering are proposed. The experimental results show that the tracking algorithm proposed in this paper has better robustness and real-time performance compared to current mainstream tracking algorithms, providing a new technical approach for research related to object detection and tracking. The tracking algorithm proposed in this article can also be seen as a framework, where the projected object can not only be the grayscale of the original pixel, but also include multi-channel color values, HOG, and other attributes.