A Framework For Automated Analysis of Surrogate Measures of Safety from Video using Deep Learning Techniques
Morten Borno Jensen, Martin Ahrnbom, Maarten C. Kruithof, Kalle Åström, Mikael Nilsson, Håkan Ardö, Aliaksei Laureshyn, Carl Johnsson, Thomas Baltzer Moeslund · VBN Forskningsportal (Aalborg Universitet) · 2019
Traffic surveillance and monitoring are gaining a lot of attention as a result of an increase of vehicles on the road and a desire to minimize accidents. In order to minimize accidents and near-accidents, it is important to be able to judge the safety of a traffic environment. It is possible to perform traffic analysis using large quantities of video data. Computer vision is a great tool for reducing the data, so that only sequences of interest are further analyzed. In this paper, we propose a cross-disciplinary framework for performing automated traffic analysis, from both a computer vision researcher’s and traffic researcher’s point-of-view. Furthermore, we present STRUDL, an open-source implementation of this framework, that computes trajectories of road users, which we use to automatically find sequences containing critical events of vehicles and vulnerable road users in an traffic intersection, which is an otherwise time-consuming task. Keywords: Computer vision, data reduction, computer aided analysis, deep learning, surveillance, tracking, detection, traffic analysis