Real time video vehicles’ tracking is very important in the intelligent traffic monitoring system. This is because of the monitoring system provide the useful information for the government to develop a high quality road system that can reduce and prevent accident or congestion. The challenging of development system is to detect the vehicle track and monitor the vehicle movement in real time condition. Therefore, Kalman filter is used for detecting vehicle in a different lighting condition (with and without shadow) and wireless sensor network (WSN) for transmitting real time video to the computer as an input. Then, real time video is a process via MATLAB which Kalman filter is running for detecting moving vehicle. Based on the algorithm developed, this study conducted the test on the road inside university. The results show that the algorithm can detect a moving vehicle appropriately


    Zugriff

    Download


    Exportieren, teilen und zitieren



    Titel :

    A Real Time System for Traffic Monitoring By Using Kalman Filter


    Beteiligte:
    Abu Bakar, E. (Autor:in) / Abdullah, A. (Autor:in)

    Erscheinungsdatum :

    29.06.2017


    Anmerkungen:

    Journal of Engineering and Technology (JET); Vol 8, No 1 (2017): JOURNAL OF ENGINEERING AND TECHNOLOGY; 103-110 ; 2289-814X ; 2180-3811


    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Klassifikation :

    DDC:    629



    Semantic Segmentation Based Real-Time Traffic Monitoring via Res-UNet Classifier and Kalman Filter

    Qureshi, Asifa Mehmood / Algarni, Asaad / Aljuaid, Hanan et al. | Springer Verlag | 2024


    A Real-Time Traffic Detection Method Based on Improved Kalman Filter

    Xun, Li / Kaikai, Nan / Yao, Liu et al. | IEEE | 2018



    Localized Extended Kalman Filter for Scalable Real-Time Traffic State Estimation

    van Hinsbergen, C. P. I. J. / Schreiter, T. / Zuurbier, F. S. et al. | IEEE | 2012


    Real-Time Extended Kalman Filter Stability Indicator

    Lassak, Kyle / Gu, Yu | AIAA | 2016