GATE: A Novel Robust Object Tracking Method Using the Particle Filtering and Level Set Method

Abstract

This paper presents a novel algorithm for robust object tracking based on the particle filtering method employed in recursive Bayesian estimation and image segmentation and optimisation techniques employed in active contour models and level set methods. The proposed Geometric Active contour-based Tracking Estimator, namely GATE, enables particle filters to track object of interest in complex environments using merely a simple feature. GATE creates a spatial prior in the state space using shape information of the tracked object. The created spatial prior is then used to filter particles in the state space in order to reshape and refine the observation distribution of the particle filtering. This improves the performance of the likelihood model in the particle filtering, so the significantly overall improvement of the particle filtering. The promising performance of our method on real video sequences are demonstrated.