1 November 2016 Improved kernel correlation filter tracking with Gaussian scale space
Author Affiliations +
Proceedings Volume 10157, Infrared Technology and Applications, and Robot Sensing and Advanced Control; 101572X (2016) https://doi.org/10.1117/12.2247210
Event: International Symposium on Optoelectronic Technology and Application 2016, 2016, Beijing, China
Recently, Kernel Correlation Filter (KCF) has achieved great attention in visual tracking filed, which provide excellent tracking performance and high possessing speed. However, how to handle the scale variation is still an open problem. In this paper, focusing on this issue that a method based on Gaussian scale space is proposed. First, we will use KCF to estimate the location of the target, the context region which includes the target and its surrounding background will be the image to be matched. In order to get the matching image of a Gaussian scale space, image with Gaussian kernel convolution can be gotten. After getting the Gaussian scale space of the image to be matched, then, according to it to estimate target image under different scales. Combine with the scale parameter of scale space, for each corresponding scale image performing bilinear interpolation operation to change the size to simulate target imaging at different scales. Finally, matching the template with different size of images with different scales, use Mean Absolute Difference (MAD) as the match criterion. After getting the optimal matching in the image with the template, we will get the best zoom ratio s, consequently estimate the target size. In the experiments, compare with CSK, KCF etc. demonstrate that the proposed method achieves high improvement in accuracy, is an efficient algorithm.
© (2016) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Shukun Tan, Shukun Tan, Yunpeng Liu, Yunpeng Liu, Yicui Li, Yicui Li, } "Improved kernel correlation filter tracking with Gaussian scale space", Proc. SPIE 10157, Infrared Technology and Applications, and Robot Sensing and Advanced Control, 101572X (1 November 2016); doi: 10.1117/12.2247210; https://doi.org/10.1117/12.2247210

Back to Top