An Improved Kernelized Correlation Filter with the Histogram in Hue-Saturation-Value Color Space for Object Tracking

Abstract

Visual tracking is a significant problem in computer vision. It requires robustness and real-time. Correlation Filter has achieved state-of-the-art results on this problem. It utilizes the circulant structure of matrix and runs at a high frame rate. However, the circulant structure of matrix is severely impacted by the background, deformation and illumination variation. Therefore, it will generate drift when the algorithm determines the final position. In this paper, we propose a strategy to tackle drift problem. We introduce the histogram of Hue-Saturation-Value (HSV) color space into the kernelized correlation filter in order to obtain a new position for redetection. Finally, we evaluate our algorithm on online tracking benchmark(OTB) and a visual object tracking benchmark(VOT). On the two datasets, the proposed approach achieves an improvement compared with the original tracker and achieves promising performance compared with other state-of-the-art trackers.