Real-Time UAV Tracking Based on PSR Stability

In this paper, a real-time unmanned aerial vehicle (UAV) tracking method is proposed. This approach builds a target representation from histograms of oriented gradient (HOG) and ColorNames features. Correlation filters have been utilized in tracking recently because of their high efficiency. To better fuse the tracking results from different features, peak-to-sidelobe ratio (PSR) is employed to evaluate robustness of our trackers. A stability measure is proposed, based on the PSR values computed over a short period of time which is also used to predict object position. Additionally, we show that the proposed PSR stability enables our tracking method to be robust to various appearance variations. The method is carried out on five UAV tracking datasets and achieves appealing results comparable to state-of-the-art trackers but at a lower computational complexity.