A comprehensive maritime benchmark dataset for detection, tracking and threat recognition

Abstract

This paper describes a new multimodal maritime dataset recorded using a multispectral suite of sensors, including AIS, GPS, radar, and visible and thermal cameras. The visible and thermal cameras are mounted on the vessel itself and surveillance is performed around the vessel in order to protect it from piracy at sea. The dataset corresponds to a series of acted scenarios which simulate attacks to the vessel by small, fast-moving boats (‘skiffs’). The scenarios are inspired by real piracy incidents at sea and present a range of technical challenges to the different stages in an automated surveillance system: object detection, object tracking, and event recognition (in this case, threats towards the vessel). The dataset can thus be employed for training and testing at several stages of a threat detection and classification system. We also present in this paper baseline results that can be used for benchmarking algorithms performing such tasks. This new dataset fills a lack of publicly available datasets for the development and testing of maritime surveillance applications.