UStark: underwater image domain-adaptive tracker based on Stark

Abstract

Though the open-air tracker has achieved an advanced level, its design is still a challenging task for degraded underwater images. Using underwater enhancement technology can improve the performance of underwater trackers. However, most underwater image enhancement methods focus on improving the visual effect rather than serving the tracker better. Therefore, we intended to explore a simple but powerful image domain-adaptive method to improve Stark’s performance by enhancing Stark’s input images. Specifically, it consists of underwater image adaptation network (UIAN) with double heads and adaptation block based on scene estimation (ABSE) that consists of three independent image processing modules without deep learning. UIAN is used to predict the category of image domain and parameters of ABSE. ABSE decodes the parameters and sequentially process underwater images in each module. The training of UIAN is independent of the training of the tracker. After training the class prediction head of UIAN first, freezing its weights, by initializing and tracking in one enhanced image and computing tracker’s loss, the parameter head can be trained to make sure UIANs hyperparameters can match the Stark tracker. The UStark proposed can adaptively process clear and degraded underwater images. Compared with Stark, UStark has improved the accuracy and success rate in typical underwater environments by 3.7% and 1.5% (blue), 5% and 3.4% (yellow), and 5.4% and 3.3% (dark), respectively. In addition, compared with other underwater image enhancement methods, our method can enhance the performance of the tracker in more categories of underwater images.