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.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

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


    Contributors:
    Wen, Nu (author) / Wang, Xiuli (author) / Guo, Jing (author) / Wang, Yankun (author) / Wang, Yang (author)


    Publication date :

    2024-01-13


    Size :

    1723363 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    A Camera-LiDAR Fusion Framework for Traffic Monitoring

    Sochaniwsky, Adrian / Huangfu, Yixin / Habibi, Saeid et al. | IEEE | 2024


    Intelligent traffic management method and system based on multi-modal perception

    YAN JUN / WANG YONGFEI / ZHANG JUHUA | European Patent Office | 2023

    Free access

    InfraDet3D: Multi-Modal 3D Object Detection based on Roadside Infrastructure Camera and LiDAR Sensors

    Zimmer, Walter / Birkner, Joseph / Brucker, Marcel et al. | IEEE | 2023


    LiDAR-Camera Fusion for Depth Enhanced Unsupervised Odometry

    Fetic, Naida / Aydemir, Eren / Unel, Mustafa | IEEE | 2022


    Reliable Infrastructural Urban Traffic Monitoring Via Lidar and Camera Fusion

    Liu, Hangxin / Furukawa, Tomonari / Tian, Yi | SAE Technical Papers | 2017