Ontology-based framework for risk assessment in road scenes using videos

Mohammad, Mahmud Abdalla, Kaloskampis, Ioannis ORCID: https://orcid.org/0000-0002-4450-4935, Hicks, Yulia Alexandrovna ORCID: https://orcid.org/0000-0002-7179-4587 and Setchi, Rossitza ORCID: https://orcid.org/0000-0002-7207-6544 2015. Ontology-based framework for risk assessment in road scenes using videos. Presented at: 19th Annual Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES-2015), Singapore, 7-9 September 2015. Procedia Computer Science. Procedia Computer Science. , vol.60 Elsevier, pp. 1532-1541. 10.1016/j.procs.2015.08.300

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Abstract

Recent advances in autonomous vehicle technology pose an important problem of automatic risk assessment in road scenes. This article addresses the problem by proposing a novel ontology tool for assessment of risk in unpredictable road traffic environment, as it does not assume that the road users always obey the traffic rules. A framework for video-based assessment of the risk in a road scene encompassing the above ontology is also presented in the paper. The framework uses as input the video from a monocular video camera only, avoiding the need for additional sometimes expensive sensors. The key entities in the road scene (vehicles, pedestrians, environment objects etc.) are organised into an ontology which encodes their hierarchy, relations and interactions. The ontology tool infers the degree of risk in a given scene using as knowledge video-based features, related to the key entities. The evaluation of the proposed framework focuses on scenarios in which risk results from pedestrian behaviour. A dataset consisting of real-world videos illustrating pedestrian movement is built. Features related to the key entities in the road scene are extracted and fed to the ontology, which evaluates the degree of risk in the scene. The experimental results indicate that the proposed framework is capable of assessing risk resulting from pedestrian behaviour in various road scenes accurately.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Publication
Status:	Published
Schools:	Engineering
Subjects:	T Technology > T Technology (General)
Uncontrolled Keywords:	Pedestrian detection; Video segmentation; Ontology
Publisher:	Elsevier
ISSN:	1877-0509
Date of First Compliant Deposit:	30 March 2016
Last Modified:	06 Jul 2023 10:20
URI:	https://orca.cardiff.ac.uk/id/eprint/79484

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