Deep learning classification applied to traffic accidents prediction

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http://hdl.handle.net/2183/31411
Except where otherwise noted, this item's license is described as Atribución-NoComercial-CompartirIgual 4.0 Internacional (CC BY-NC-SA 4.0)
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Deep learning classification applied to traffic accidents predictionDate
2022Citation
Coll-Josifov, R., Masip-Álvarez, A., Lavèrnia-Ferrer, D. (2022) Deep learning classification applied to traffic accidents prediction. XLIII Jornadas de Automática: libro de actas, pp.964-971 https://doi.org/10.17979/spudc.9788497498418.0964
Abstract
[Abstract] In this paper, YOLOv4 neural networks are trained with the goal of detecting and classifying objects from a street as seen from a drone. These have been trained on the VisDrone dataset, which is firstly validated through a custom-made graphic user interface. Then, several tests regarding performance, dataset composition and contrast have been carried out on the trained models. Results are compared to those from other previously existing models in order to evaluate their performance and analyse their shortcomings. The trained models are then used to detect and classify objects in a city scenario in real-time. Finally, an algorithm is proposed to track the objects, infer their future trajectories and predict potential collisions from the expected trajectories.
Keywords
You Only Look Once (YOLO)
Computer vision
Deep learning
Accident prediction
Computer vision
Deep learning
Accident prediction
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Rights
Atribución-NoComercial-CompartirIgual 4.0 Internacional (CC BY-NC-SA 4.0)
https://creativecommons.org/licenses/by-nc-sa/4.0/deed.es
ISBN
978-84-9749-841-8