On the improvement of foreground-background model-based object tracker

Abstract

In this study, the authors propose two kinds of improvements to a baseline tracker that employs the tracking-by-detection framework. First, they explore different feature spaces by employing features commonly used in object detection to improve the performance of detector in feature space. Second, they propose a robust scale estimation algorithm that estimates the size of the object in the current frame. Their experimental results on the challenging online tracking benchmark-13 dataset show that reduced dimensionality histogram of oriented gradients boosts the performance of the tracker. The proposed scale estimation algorithm provides a significant gain and reduces the failure of the tracker in challenging scenarios. The improved tracker is compared with 13 state-of-the-art trackers. The quantitative and qualitative results show that the performance of the tracker is comparable with the state of the art against initialisation errors, variations in illumination, scale and motion, out-of-plane and in-plane rotations, deformations and low resolution.