Temporally-adjusted correlation filter-based tracking

Abstract

Abstract Recently, discriminative correlation filter (DCF) has been wildly studied and adopted in visual object tracking task. Since the convolution operation can be efficiently computed through fast Fourier transform (FFT), DCF trackers achieve the outstanding results while maintaining a very high computational performance. Lots of research efforts have been devoted to improving the tracking capability of DCF-based trackers, such as exploring new features or dealing with scale changes. As visual object tracking is naturally an incremental procedure, DCF trackers are inevitably suffering from drifting phenomenon caused by the accumulated small errors during the tracking process. In this paper, we proposed a temporally-adjusted correlation filter (TCF) tracking method to effectively address the drifting problem. By taking advantage of temporal information among the previous states of the target, our approach is able to refine the traditional DCF model during the tracking procedure and greatly reduce the risk of drifting. The experimental results on the challenging OTB-2013 and OTB-2015 dataset show that the proposed strategy is very promising.