| Autor: |
Jensen, Morten Bornø, Ahrnbom, Martin, Kruithof, Maarten, Åström, Kalle, Nilsson, Mikael, Ardö, Håkan, Laureshyn, Aliaksei, Johnsson, Carl, Moeslund, Thomas B. |
| Jazyk: |
angličtina |
| Rok vydání: |
2019 |
| Zdroj: |
Jensen, M B, Ahrnbom, M, Kruithof, M, Åström, K, Nilsson, M, Ardö, H, Laureshyn, A, Johnsson, C & Moeslund, T B 2019, ' A Framework For Automated Analysis of Surrogate Measures of Safety from Video using Deep Learning Techniques ', Transportation Research Board. Annual Meeting Proceedings, pp. 281-306 . |
| Popis: |
Traffic surveillance and monitoring are gaining a lot of attention as a result of anincrease of vehicles on the road and a desire to minimize accidents. In order to minimizeaccidents and near-accidents, it is important to be able to judge the safety of atraffic environment. It is possible to perform traffic analysis using large quantitiesof video data. Computer vision is a great tool for reducing the data, so that only sequencesof interest are further analyzed. In this paper, we propose a cross-disciplinaryframework for performing automated traffic analysis, from both a computer vision researcher’sand traffic researcher’s point-of-view. Furthermore, we present STRUDL,an open-source implementation of this framework, that computes trajectories of roadusers, which we use to automatically find sequences containing critical events ofvehicles and vulnerable road users in an traffic intersection, which is an otherwisetime-consuming task.Keywords: Computer vision, data reduction, computer aided analysis, deep learning,surveillance, tracking, detection, traffic analysis |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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