Saliency Guided Siamese Attention Network for Infrared Ship Target Tracking

Due to the lack of discriminative features in infrared images, most of existing trackers cannot separate a target from its background. There are some studies on generating discriminative features where feature fusion and attention are applied to enhance targets. However, the saliency information and information interaction which assist in locating the targets is ignored. To improve the accuracy of infrared ship target tracking, we propose a saliency guided Siamese attention network (SGSiamAttn). The main contribution is to design a saliency prediction network that obtains the saliency map of a search region and followed by a saliency enhancement network to highlight the target. With the saliency information, our network is able to perceive the entire target, which improves the discriminative ability and the tracking accuracy. Meanwhile, a local-to-global correlation module is applied before the saliency prediction network, aiming to refine the correlation map while suppressing non-target interferences. We also impose a shared cross-correlation module on the region proposal network. By sharing the correlation map in the classification and regression branches, it enhances information interaction between the two tasks and reduces the computational cost. As there are limited number of infrared ship tracking datasets publicly available, we construct a new infrared ship dataset (ISD) which includes 16 different types of ships and 7,872 video frames with manual annotations. The experimental results on ISD and other three public datasets, namely VOT-TIR2015, PTB-TIR, and LSOTB-TIR, demonstrate that our tracker achieves superior performance in terms of accuracy, expected average overlap, success, and precision.