Automated operator support for vessel threat assessment

Wyke Huizinga; Michel van Lier; Maarten Kruithof; Judith Dijk

doi:10.1117/12.2600802

15 September 2021 Automated operator support for vessel threat assessment

Wyke Huizinga, Michel van Lier, Maarten Kruithof, Judith Dijk

Proceedings Volume 11870, Artificial Intelligence and Machine Learning in Defense Applications III; 1187006 (2021) https://doi.org/10.1117/12.2600802
Event: SPIE Security + Defence, 2021, Online Only

Abstract

Early threat assessment of vessels is an important surveillance task during naval operations. Whether a vessel is a threat depends on a number of aspects. Amongst those are the vessel class, the closest point of approach (CPA), the speed and direction of the vessel and the presence of possible threatening items on board the vessel such as weapons. Currently, most of these aspects are observed by operators viewing the camera imagery. Whether a vessel is a potential threat will depend on the final assessment of the operator. Automated analysis of electro-optical (EO) imagery for aspects of potential threats during surveillance can support the operator during observation. This can release the operator from continuous guard and provide him with the tools to provide a better overview of possible threats in the surroundings during a surveillance task. In this work, we apply different processing algorithms, including detection, tracking and classification, on recorded multi-band EO imagery in a harbor environment with many small vessels. With the results we aim to automatically determine the vessel’s CPA, number of people on board and the presence of possibly threatening items on board of the vessel. Hereby we show that our algorithms can support the operator in assessing whether a vessel poses a threat or not.

Conference Presentation

Citation Download Citation

Wyke Huizinga, Michel van Lier, Maarten Kruithof, and Judith Dijk "Automated operator support for vessel threat assessment", Proc. SPIE 11870, Artificial Intelligence and Machine Learning in Defense Applications III, 1187006 (15 September 2021); https://doi.org/10.1117/12.2600802

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