Object Tracking Based on Probabilistic State Estimation with Kernelized Correlation Filter

Abstract

In this paper, we propose a novel object tracking method to avoid tracking failures. The proposed method is based on probabilistic state estimation, and uses Kernelized Correlation Filter (KCF) for evaluating a likelihood. The experimental result shows that the proposed method achieves the superior performance to state-of-the-art methods.