基于Radon变换数据外观建模的目标跟踪

Target Tracking Based on Radon Transform Data Appearance Modeling

摘要: 本文主要针对复杂环境下目标跟踪中一个重要挑战——算法运行的实时性，研究一种新的基于Radon变换数据的目标外观模型，并将其引入到相关滤波框架中进行滤波模板训练，并提出了一种基于相关滤波的快速跟踪算法及目标尺度更新方案。实验结果表明，本文提出的跟踪算法相较于当前主流的跟踪算法具有更好的鲁棒性及实时性，为目标检测与跟踪等相关研究提供了新的技术途径。本文所提出的跟踪算法也可以视为一种框架，投影的对象不仅仅可以是原始像素的灰度，还可以是多通道颜色值、HOG等其它属性。

Abstract: 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.