Object tracking using Langevin Monte Carlo particle filter and locality sensitive histogram based likelihood model

Abstract

Visual object tracking is a challenging research task in computer vision community which has been intensively studied by researchers in the past decades. Among all of the existing methods, particle filter based methods have gained special attention due to its ability to handle highly nonlinear/non-Gaussian multi-modality models. This paper proposes a robust particle filter based tracking method based on the Langevin Monte Carlo sampling. The Langevin Monte Carlo sampling method leverages the gradient of the posterior probability distribution to draw new particles. Meanwhile, an auxiliary momentum variable is introduced to ensure that the proposed sample cannot be trapped in local mode of the posterior distribution. As for the likelihood model, we introduce the locality sensitive histogram based model to handle the severe appearance variations induced by illumination change, partial occlusion or other factors. We compare the proposed method with several popular tracking methods from qualitative and quantitative perspectives. The experimental results show that the proposed method outperforms its counterparts.