Keeping the safe distance from the leading vehicle is crucial for transportation companies with a fleet of old cars. While modern Advanced Driver Assistant Systems (ADAS) might be able to estimate the distance from the front-leading vehicle, traditional ADAS do not usually offer this feature. An alternative solution is to monitor the distance using smartphones that are attached to a place such as a sun visor. The basic idea behind this approach is to detect the front-leading vehicle using the smartphone camera and estimate its distance from the car. Although SSD can achieve real-time performance on powerful GPUs, it remains challenging to run this model in real-time on mobile devices. In this paper, we propose a monocular distance estimator for forward-leading vehicles using a smartphone which is faster and more accurate than the state-of-the-art SSD detector. Specifically, we propose a layer-wise method to generate more efficient default boxes for the SSD and develop a lightweight method for estimating the distance accurately. Our experiments show that the proposed method reduces the number of default boxes by an average of 38.4% while it improves the detection rate and the processing speed compared to the original SSD. Moreover, our monocular distance estimator provides a proper safety buffer zone when the distance is greater than 20 meters. A sample video is available at https://youtu.be/-ptvfabBZWA.


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    Titel :

    A Monocular Forward Leading Vehicle Distance Estimation using Mobile Devices


    Beteiligte:
    Wen, Wen (Autor:in) / Aghdam, Hamed H. (Autor:in) / Wang, Yong (Autor:in) / Laganiere, Robert (Autor:in) / Petriu, Emil (Autor:in)


    Erscheinungsdatum :

    2020-10-19


    Format / Umfang :

    3630748 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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