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An Urban Traffic Dataset Composed of Visible Images and Their Semantic Segmentation Generated by the CARLA Simulator

Author

Listed:
  • Sergio Bemposta Rosende

    (Department of Science, Computing and Technology, Universidad Europea de Madrid, 28670 Villaviciosa de Odón, Spain)

  • David San José Gavilán

    (Intelligent Control Systems Research Group, Universidad Europea de Madrid, 28670 Villaviciosa de Odón, Spain)

  • Javier Fernández-Andrés

    (Department of Industrial and Aerospace Engineering, Universidad Europea de Madrid, 28670 Villaviciosa de Odón, Spain)

  • Javier Sánchez-Soriano

    (Escuela Politécnica Superior, Universidad Francisco de Vitoria, 28223 Pozuelo de Alarcón, Spain)

Abstract

A dataset of aerial urban traffic images and their semantic segmentation is presented to be used to train computer vision algorithms, among which those based on convolutional neural networks stand out. This article explains the process of creating the complete dataset, which includes the acquisition of the images, the labeling of vehicles, pedestrians, and pedestrian crossings as well as a description of the structure and content of the dataset (which amounts to 8694 images including visible images and those corresponding to the semantic segmentation). The images were generated using the CARLA simulator (but were like those that could be obtained with fixed aerial cameras or by using multi-copter drones) in the field of intelligent transportation management. The presented dataset is available and accessible to improve the performance of vision and road traffic management systems, especially for the detection of incorrect or dangerous maneuvers.

Suggested Citation

  • Sergio Bemposta Rosende & David San José Gavilán & Javier Fernández-Andrés & Javier Sánchez-Soriano, 2023. "An Urban Traffic Dataset Composed of Visible Images and Their Semantic Segmentation Generated by the CARLA Simulator," Data, MDPI, vol. 9(1), pages 1-12, December.
    • Handle: RePEc:gam:jdataj:v:9:y:2023:i:1:p:4-:d:1306898
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