A Deep Hyper Siamese Network for Real-Time Object Tracking

Abstract

Siamese networks have drawn increasing interest in the field of visual object tracking due to their balance of precision and efficiency. However, Siamese trackers use relatively shallow backbone networks, such as AlexNet, and therefore do not take full advantage of the capabilities of modern deep convolutional neural networks (CNNs). Moreover, the feature representations of the target object in a Siamese tracker are extracted through the last layer of CNNs and mainly capture semantic information, which causes the tracker's precision to be relatively low and to drift easily in the presence of similar distractors. In this paper, a new nonpadding residual unit (NPRU) is designed and used to stack a 22-layer deep ResNet, referred as ResNet22. After utilizing ResNet22 as the backbone network, we can build a deep Siamese network, which can greatly enhance the tracking performance. Considering that the different levels of the feature maps of the CNN represent different aspects of the target object, we aggregated different deep convolutional layers to make use of ResNet22's multilevel feature maps, which can form hyperfeature representations of targets. The designed deep hyper Siamese network is named DHSiam. Experimental results show that DHSiam has achieved significant improvement on multiple benchmark datasets.