Robust Kernelized Correlation Filter using Adaptive Feature Weight

Abstract

In this paper, we propose a robust tracking method for fast motion and background noise via improved kernelized correlation filters (KCFs). The proposed tracking algorithm consists of four steps: i) generate a Gaussian blurred image of the input image, ii) compute each correlation response and weight update score, iii) compute adaptive feature weight using a weight update score, and iv) integrate features using the adaptive feature weight, and train a correlation filter using the integrated features. As a result, the proposed tracking method can robustly track fast motion and background noise. The proposed tracking algorithm can be applied to video surveillance systems and intelligent vehicles.