Visual object tracking based on Siamese network and online patch filters

Abstract

Visual object tracking is one of the popular research topics in computer vision. It has a wide range of application scenarios. Although recent approaches based on siamese network have achieved good performance, similar interference and non-real-time speed are still very challenging problems. In this paper, an online Patch Filter Network (OPFNet) is proposed, the online patch filters learned from the target can introduce the local detailed features and avoid the interference of similar objects. In addition, in order to enhance the generalization ability of the tracker trained with small scale dataset, an image mix-up method for augmentation is proposed during offline training process. These improvements are proved to be effective by experiments and can be applied to existed siamese tracking methods