Autonomous driving holds great promise in addressing traffic safety concerns by leveraging artificial intelligence and sensor technology. Multi-Object Tracking plays a critical role in ensuring safer and more efficient navigation through complex traffic scenarios. This paper presents a novel deep learning-based method that integrates radar and camera data to enhance the accuracy and robustness of Multi-Object Tracking in autonomous driving systems. The proposed method leverages a Bi-directional Long Short-Term Memory network to incorporate long-term temporal information and improve motion prediction. An appearance feature model inspired by FaceNet is used to establish associations between objects across different frames, ensuring consistent tracking. A tri-output mechanism is employed, consisting of individual outputs for radar and camera sensors and a fusion output, to provide robustness against sensor failures and produce accurate tracking results. Through extensive evaluations of real-world datasets, our approach demonstrates remarkable improvements in tracking accuracy, ensuring reliable performance even in low-visibility scenarios.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Deep Learning-Based Robust Multi-Object Tracking via Fusion of mmWave Radar and Camera Sensors


    Contributors:

    Published in:

    Publication date :

    2024-11-01


    Size :

    4325176 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    3D Multi-Object Tracking Based on Radar-Camera Fusion

    Lin, Zihao / Hu, Jianming | IEEE | 2022


    A DNN-LSTM based Target Tracking Approach using mmWave Radar and Camera Sensor Fusion

    Sengupta, Arindam / Jin, Feng / Cao, Siyang | IEEE | 2019


    Deep Learning-based Radar, Camera, and Lidar Fusion for Object Detection

    Nobis, Felix Otto Geronimo | TIBKAT | 2022

    Free access

    CFTrack: Center-based Radar and Camera Fusion for 3D Multi-Object Tracking

    Nabati, Ramin / Harris, Landon / Qi, Hairong | IEEE | 2021


    People Tracking by Cooperative Fusion of RADAR and Camera Sensors

    Dimitrievski, Martin / Jacobs, Lennert / Veelaert, Peter et al. | IEEE | 2019