Ensemble Of adaptive correlation filters for robust visual tracking

Correlation filters have recently been popular due to their success in short-term single-object tracking as well as their computational efficiency. Nevertheless, the appearance model of a single correlation filter based tracking algorithm quickly forgets the past poses of the target object due to the updates over time. To overcome this undesired forgetting, our approach is to run trackers with separate models simultaneously. Hence, we propose a novel tracker relying on an ensemble of correlation filters, where the ensemble is obtained via a decision tree partitioning in the object appearance space. Our technique efficiently searches among the ensemble trackers and activates the ones which are most specialized on the current object appearance. Our tracking method is capable of switching frequently in the ensemble. Thus, an inherently adaptive and non-linear learning rate is achieved. Moreover, we demonstrate the superior performance of our method in benchmark video sequences.