The development of artificial vision systems to support driving has been of great interest in recent years, especially after new learning models based on deep learning. In this work, a framework is proposed for detecting road speed anomalies, taking as reference the driving vehicle. The objective is to warn the driver in real-time that a vehicle is overtaking dangerously to prevent a possible accident. Thus, taking the information captured by the rear camera integrated into the vehicle, the system will automatically determine if the overtaking that other vehicles make is considered abnormal or dangerous or is considered normal. Deep learning-based object detection techniques will be used to detect the vehicles in the road image. Each detected vehicle will be tracked over time, and its trajectory will be analyzed to determine the approach speed. Finally, statistical regression techniques will estimate the degree of anomaly or hazard of said overtaking as a preventive measure. This proposal has been tested with a significant set of actual road sequences in different lighting conditions with very satisfactory results.


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    Titel :

    Vehicle Overtaking Hazard Detection over Onboard Cameras Using Deep Convolutional Networks


    Weitere Titelangaben:

    Lect. Notes in Networks, Syst.


    Beteiligte:
    García Bringas, Pablo (Herausgeber:in) / Pérez García, Hilde (Herausgeber:in) / Martinez-de-Pison, Francisco Javier (Herausgeber:in) / Villar Flecha, José Ramón (Herausgeber:in) / Troncoso Lora, Alicia (Herausgeber:in) / de la Cal, Enrique A. (Herausgeber:in) / Herrero, Álvaro (Herausgeber:in) / Martínez Álvarez, Francisco (Herausgeber:in) / Psaila, Giuseppe (Herausgeber:in) / Quintián, Héctor (Herausgeber:in)

    Kongress:

    International Workshop on Soft Computing Models in Industrial and Environmental Applications ; 2022 ; Bilbao, Spain September 05, 2022 - September 07, 2022



    Erscheinungsdatum :

    12.10.2022


    Format / Umfang :

    10 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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