Adaptive Online Learning Based Robust Visual Tracking

Accurate location estimation of a target is a classical and very popular problem in visual object tracking, for which correlation filters have been proven highly effective in real-time scenarios. However, the great variation of the target's appearance and the surrounding background throughout a video sequence would lead to failure tracking for the sake of the model drift, using trackers based on correlation filters. In our approach, we present a simple and fast method to improve the robustness of the model based on sum of template and pixel-wise learners (Staple). On the one hand, a confidence regression model is established to adjust adaptively the model online learning rate to alleviate the model drift. On the other hand, instead of likelihood, the scale with maximal posterior probability is selected as the target scale to obtain the more accurate estimation. Extensive experimental results demonstrate that the proposed approach performs favorably against several state-of-the-art algorithms on large-scale challenging benchmark data sets at speed in excess of 42 frames/s.