Object tracking using Langevin Monte Carlo particle filter and locality sensitive histogram based likelihood model. (February 2018) - Wang, Fasheng; Lin, Baowei; Zhang, Junxing; Li, Xucheng
Highlights: We propose to use the Langevin Monte Carlo Sampling method to sample from the posterior distribution. We adopt the locality sensitive histogram (LSH) based likelihood model for template based object representation. Each candidate is evaluated by computing the distance between its LSH and the templates LSH. The template LSH is updated online. The LMC and LSH based likelihood model is incorporated within the particle filter tracking framework to build a robust tracker. We demonstrate the performance of the proposed method based on comprehensive analysis of the experimental results. Graphical abstract: 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.