3D object detection from monocular camera videos constitutes a critical research domain. Achieving robust 3D object detection in databases lacking annotated information poses a highly challenging task. This paper proposes a simple yet effective transfer learning approach, integrating data alignment, 3D object detection, and dynamic result correction. Vanishing point detection is employed to infer camera angles in diverse scenes, and preprocessing of new data is conducted by considering the camera's pitch angle and vanishing point position. Subsequently, MonoDETR are applied for depth estimation and 3D object detection in monocular videos. Building upon the detection results, dynamic correction is achieved through inter-frame assistance, culminating in the final 3D object information. Validation on the TUAT Near-Miss Incident Database demonstrates the efficacy of the proposed approach. The results indicate a substantial reduction in the cost of annotating new domain data while simultaneously enhancing detection accuracy and robustness. Integration with onboard diagnostics (OBD) data allows the reconstruction of information about various traffic participants in hazardous scenarios, providing valuable insights for in-depth analysis of accident causes.


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    Title :

    Robust 3D On-Road Object Detection and Distance Estimation for Active Vehicle Control Systems Based on Monocular Camera Image Data


    Additional title:

    Lect.Notes Mechanical Engineering



    Conference:

    Advanced Vehicle Control Symposium ; 2024 ; Milan, Italy September 01, 2024 - September 05, 2024



    Publication date :

    2024-10-04


    Size :

    7 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English





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