Abstract/Summary

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 vis- ible 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 ves- sel 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.