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

Patino, L., Cane, T. and Ferryman, J. (2022) A comprehensive maritime benchmark dataset for detection, tracking and threat recognition. In: 17th IEEE Int'l Conf on Advanced Video and Signal-based Surveillance (AVSS 2021), 16-19 NOV 2021, Virtual,

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To link to this item DOI: 10.1109/AVSS52988.2021.9663739


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.

Item Type:Conference or Workshop Item (Paper)
Divisions:Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
ID Code:101889

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