Adaptive particle filter tracker combining kernel distribution to particle filtering to better enhance object tracking

Abstract

Object tracking research is one of the most important research fields in computer vision, which allows us to resolve several well-defined issues. Of course, researchers have to deal with many challenges when one or multiple objects need to be tracked, for instance when the target is partially or fully occluded, background clutter or even some target region is blurred. In this paper, we will present a novel approach for a single object tracking that combines particle filter algorithm and kernel distribution, , whose name is an adaptive particle filter tracker. We will demonstrate that the use of particle filter combined to kernel distribution inside the resampling process will provide more accurate object localization within a research area. Furthermore, its average error for target localization was significantly lower than 17.37 pixels as the mean value. We have conducted several experiments on real video sequences and compared acquired results to other existing state of the art trackers to demonstrate the effectiveness of the adaptive particle filter tracker.