A Camera-based Deep-Learning Solution for Visual Attention Zone Recognition in Maritime Navigational Operations

Abstract

The visual attention of navigators is imperative to understand the logic of navigation as well as the surveillance of navigators' status and operation. Current studies are implemented with the help of wearable eye-tracker glasses; yet, the high expenditure demanded by such equipment and service and its limitations on usability have impeded related research from further development. In this letter, the authors propose a framework, which is the first attempt in the maritime domain, to provide a camera-based deep-learning (CaBDeeL) visual attention recognition solution that outperforms the intrusive eye tracker regarding its shortcomings. A wide-angle camera is configured in front of the navigator in the advanced ship-bridge simulator in a way that visual attention reflected by their facial and head movements is captured in the front view. A pair of eye-tracker glasses is used to classify the captured visual attention images, which then form the primary database. During the process of classifying camera-captured images, a convolutional neural network (CNN) is built as an automatic classifier. The CNN is applied to two scenarios, and it shows an overall 95 % accuracy.