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arXiv · 2004.11824

Detecting Unsigned Physical Road Incidents from Driver-View Images

Alex Levering·Martin Tomko·Devis Tuia·Kourosh Khoshelham
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Abstract

Safety on roads is of uttermost importance, especially in the context of autonomous vehicles. A critical need is to detect and communicate disruptive incidents early and effectively. In this paper we propose a system based on an off-the-shelf deep neural network architecture that is able to detect and recognize types of unsigned (non-placarded, such as traffic signs), physical (visible in images) road incidents. We develop a taxonomy for unsigned physical incidents to provide a means of organizing and grouping related incidents. After selecting eight target types of incidents, we collect a dataset of twelve thousand images gathered from publicly-available web sources. We subsequently fine-tune a convolutional neural network to recognize the eight types of road incidents. The proposed model is able to recognize incidents with a high level of accuracy (higher than 90%). We further show that while our system generalizes well across spatial context by training a classifier on geostratified data in the United Kingdom (with an accuracy of over 90%), the translation to visually less similar environments requires spatially distributed data collection. Note: this is a pre-print version of work accepted in IEEE Transactions on Intelligent Vehicles (T-IV;in press). The paper is currently in production, and the DOI link will be added soon.

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Alex Levering, Martin Tomko, Devis Tuia, Kourosh Khoshelham. 2020-04-24. Detecting Unsigned Physical Road Incidents from Driver-View Images. https://doi.org/10.1109/tiv.2020.2991963

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