Fusion of Template Matching and Foreground Detection for Robust Visual Tracking

In this paper, we present an end-to-end framework for visual tracking that contains fully convolutional template matching network and fully convolutional foreground detection network. It fuses the response maps of foreground detection and template matching for robust tracking and it can inherits all the merits of them. Besides, our network don't need additional datasets to train and only object information in the first frame is needed in training stage. We conduct extensive experiments on OTB2013 and OTB2015 and our tracker achieve state-of-the-art performance in both efficiency and accuracy.