Robust visual tracking via ranking SVM

In this paper, we tackle the tracking problem in a quite other viewpoint, ranking. First, the ranking SVM is employed to learn a ranking function. Then, the ranking function ranks every instance sampled from the next frame, and the instance with the most preferred ranking score is assumed to be the object. Experiments of extensively quantitative and qualitative comparisons on public videos show the superior performance of our tracker over several state-of-the-art tracking algorithms.