The Hearing Impaired People (HIP) cannot distinguish the sound from a moving vehicle approaching from their behind. Since, it is difficult for hearing impaired to hear and judge sound information and they often encounter risky situations while they are in outdoor. If HIPs can successfully get sound information through some machine interface, dangerous situation will be avoided. Generally the profoundly deaf people do not use any hearing aid which does not provide any benefit. This paper presents, simple statistical features are used to classify the vehicle type and its distance based on sound signature recorded from the moving vehicles. An experimental protocol is designed to record the vehicle sound under different environment conditions and also at different speed of vehicles. Basic statistical features such as the standard deviation, Skewness, Kurtosis and frame energy have been used to extract the features. Probabilistic neural network (PNN) models are developed to classify the vehicle type and its distance. The effectiveness of the network is validated through stimulation.


    Zugriff

    Zugriff über TIB

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Moving Vehicle Detection Using Time Domain Statistical Features


    Beteiligte:


    Erscheinungsdatum :

    2013


    Format / Umfang :

    5 Seiten




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Print


    Sprache :

    Englisch




    Automatic vehicle detection using local features - a statistical approach

    Wang, Chi-Chen-Raxle / Lien, J.J.J. | Tema Archiv | 2008


    Automatic Vehicle Detection Using Local Features—A Statistical Approach

    Chi-Chen Raxle Wang, / Lien, J.-J.J. | IEEE | 2008



    Traffic flow statistical method based on urban moving vehicle detection

    MO BIWEN | Europäisches Patentamt | 2023

    Freier Zugriff

    SYSTEM AND METHOD FOR MEASURING MOVING VEHICLE INFORMATION USING ELECTRICAL TIME DOMAIN REFLECTOMETRY

    HANSON RANDAL LEROY / LOCKERBIE MICHAEL DAVID / MEIER IAN ROBERT et al. | Europäisches Patentamt | 2021

    Freier Zugriff