Modified Particle Filtering using Foreground Separation and Confidence for Object Tracking

Abstract

Particle filter is a widely used framework for object tracking, but it is vulnerable when its observation model is based on visual appearance. In this paper, we propose a modified particle filtering that makes use of foreground regions and their pixel-based confidences that are likely to be foreground; the foreground regions are used for preventing generations of particle in the background and the pixel-based confidences are enable to enhance the similarity between foreground and observation models. We evaluate the performance on five datasets and show that the proposed approach outperforms a number of state-of-the-art object tracking methods.