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Correlation filter has been proven to be an effective tool for a number ofapproaches in visual tracking, particularly for seeking a good balance betweentracking accuracy and speed. However, correlation filter based models aresusceptible to wrong updates stemming from inaccurate tracking results. Todate, little effort has been devoted towards handling the correlation filterupdate problem. In this paper, we propose a novel approach to address thecorrelation filter update problem. In our approach, we update and maintainmultiple correlation filter models in parallel, and we use deep reinforcementlearning for the selection of an optimal correlation filter model among them.To facilitate the decision process in an efficient manner, we propose adecision-net to deal target appearance modeling, which is trained throughhundreds of challenging videos using proximal policy optimization and alightweight learning network. An exhaustive evaluation of the proposed approachon the OTB100 and OTB2013 benchmarks show that the approach is effective enoughto achieve the average success rate of 62.3% and the average precision score of81.2%, both exceeding the performance of traditional correlation filter basedtrackers.

Correlation Filter Selection for Visual Tracking Using Reinforcement Learning