Hierarchical convolutional features for visual tracking via two combined color spaces with SVM classifier

Abstract

As the state-of-the-art object trackers majority, hierarchical convolutional features (HCF) cannot recover tracking processes from problems of drifting caused by several challenges, especially by heavy occlusion, scale variation, and illumination variation. In this paper, we present a new effective method with the aim of treating these challenges robustly based on two principal tasks. First, we infer the target location using multichannel correlation maps, resulting from the combination of five learned correlation filters with convolutional features. In order to handle the illumination variation and get more rich features, we exploit an HSV energy condition to control the use of two color spaces, RGB and HSV. Second, we use the histogram of gradient features to learn another correlation filter in order to estimate the scale variation. Furthermore, we exploit an online training SVM classifier for target re-detecting in failure cases. The extensive experiments on a commonly used tracking benchmark dataset justify that our tracker significantly improves HCF and outperforms the state-of-the-art methods.