A Robust Visual Tracking Method through Deep Learning Features

Abstract

Object tracking is one of the most important components in many applications of computer vision. Among numerous methods developed in recent years, correlation filter based trackers have aroused increasing interests and have achieved extremely compelling results in different competitions and benchmarks. In this paper, we propose a novel approach based on correlation filter framework for robust scale estimation through deep learning features. Experiments are performed on benchmark sequences with occlusion, background clutter, pose change and significant scale variations. Our results show that the proposed algorithm performs favorably against state-of-the-art methods in terms of accuracy and robustness.