Object tracking for unmanned aerial vehicle applications in outdoor scenes is a very complex problem. In videos captured by unmanned aerial vehicle, due to frequent variation in illumination, motion blur, image noise, deformation, lack of image texture, occlusion, fast motion, and other degradations, most tracking methods will lead to failure. The article focuses on the object tracking in severely degraded videos. To deal with those various degradations, a real-time object tracking method for high dynamic background is developed. By integrating histogram of oriented gradient, RGB histogram and motion histogram into a novel statistical model, our method can robustly track the target in unmanned aerial vehicle captured videos. Compared to those existing methods, our proposed approach costs less resource in the tracking, significantly increases the tracking speed, and runs faster than state-of-the-art methods. Also, our approach achieved satisfactory tracking results on the challenging visual tracking benchmark, object tracking benchmark 2013, the supplementary experiments demonstrates that our method is more effective and accurate than other methods.
Object Tracking in Unmanned Aerial Vehicle Videos via Multifeature Discrimination and Instance-Aware Attention Network