Robust visual tracking via samples ranking

Abstract

Abstract In recent years, deep convolutional neural networks (CNNs) have achieved great success in visual tracking. To learn discriminative representations, most of existing methods utilize information of image region category, namely target or background, and/or of target motion among consecutive frames. Although these methods demonstrated to be effective, they ignore the importance of the ranking relationship among samples, which is able to distinguish one positive sample better than another positive one or not. This is especially crucial for visual tracking because there is only one best target candidate among all positive candidates, which tightly bounds the target. In this paper, we propose to take advantage of the ranking relationship among positive samples to learn more discriminative features so as to distinguish closely similar target candidates. In addition, we also propose to make use of the normalized spatial location information to distinguish spatially neighboring candidates. Extensive experiments on challenging image sequences demonstrate the effectiveness of the proposed algorithm against several state-of-the-art methods.