The advances in Artificial Intelligence have provided powerful tools for perception in road applications, such as Traffic Monitoring or Autonomous vehicles. However, detecting and classifying objects from top-view or aerial imagery becomes more challenging, especially in urban cities; due to the variation in size and scale of the objects; in addition to the lack of the datasets that contain information about the object classes and their 3D positions. This paper introduces a novel algorithm to refine the semantic images captured from CARLA simulator; refining the semantic images, creating a new dataset with more classes of the objects, and calculating its 3D bounding box positions. The proposed method and dataset have been validated by training a CNN - based 2D object detector, and inferring with real aerial images from VisDrone-2019, and from fixed infrastructure cameras i.e. cameras mounted on Traffic lights. The obtained results illustrates the improvements of using the proposed dataset in the object detection process, with real top-view images in complex environments.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Reducing the Breach between Simulated and Real Data for Top View Images


    Contributors:


    Publication date :

    2021-07-11


    Size :

    3988343 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    REDUCING THE BREACH BETWEEN SIMULATED AND REAL DATA FOR TOP VIEW IMAGES

    Astudillo, Armando / Al-Kaff, Abdulla / Madridano, Ángel et al. | British Library Conference Proceedings | 2021


    REDUCING AND PROCESSING SIMULATED AND REAL-WORLD RADAR DATA

    WANG KE | European Patent Office | 2024

    Free access

    Security breach

    Scoltock,J. | Automotive engineering | 2015


    Overtopping and breaching of dikes – Breach profile and breach flow

    Schmocker, Lukas / Hager, Willi H. | HENRY – Federal Waterways Engineering and Research Institute (BAW) | 2010

    Free access

    Breach security stonewall

    Online Contents | 1993