Intelligent Traffic Systems play a crucial role in modern urban environments, aiming to improve traffic flow, reduce congestion, and enhance overall road safety. And the integration of lidar and camera has gained significant attention in recent years, particularly in the field of Intelligent Traffic Systems. This paper proposes a three-dimensional space static and dynamic object recognition method based on continuous point cloud frame statistics and a vision-based 3D object positioning method to achieve four-dimensional state estimation and prediction and object identification and positioning of traffic object. This method can provide a 4D object information compared with a single lidar or camera sensor, and can compute a 3D position only using a 2D video. We discuss the challenges and opportunities associated with their fusion, emphasizing the potential for improved perception, object recognition, and overall scene understanding in various applications, including autonomous vehicles, robotics, and environmental monitoring.


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    Titel :

    Multi-modal Fusion of LiDAR and Camera Sensors for Enhanced Perception in Intelligent Traffic Systems


    Beteiligte:
    Wen, Nu (Autor:in) / Wang, Xiuli (Autor:in) / Guo, Jing (Autor:in) / Wang, Yankun (Autor:in) / Wang, Yang (Autor:in)


    Erscheinungsdatum :

    13.01.2024


    Format / Umfang :

    1723363 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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