摘要：Current tracking methods rely on color-, intensity-, and edge-based features to compute a description of an image region. These approaches are not well-suited for low-quality images such as mm-wave data from full-body scanners. In order to perform tracking in such challenging grayscale images, we propose several enhancements and extensions to the Visual Tracking Decomposition (VTD) by Kwon and Lee. A novel region descriptor, which uses texture-based features, is presented and integrated into VTD. We improve VTD by adding a sophisticated weighting scheme for observations, better motion models, and a more realistic way for sampling and interaction. Our method not only outperforms VTD on mm-wave data but also has comparable results on normal-quality images. We are confident that our region descriptor can easily be extended to other kinds of features and applications such that tracking can be performed in a large variety of image data, especially low-resolution, low-illumination and noisy images.
