In times, urban centers are growing at a high rate. Growing with them is a road traffic jam. Traffic jams, especially at peak hours, became routine. As a result, traffic management is one of the foremost pressing issues in today's towns. Several alternatives are being sought to affect the matter. These include expanding road networks, regulating the number of vehicles on the roads, and deployment of Intelligent Transportation Systems (ITSs). Aside from the ITSs, the opposite alternatives (however significant) have many practical challenges in their implementation. ITSs have supported a good range of technologies like loop sensors and video surveillance systems. Vision-based ITSs have proved advantageous over the standard methods supported loop sensors. In these modern systems, video surveillance cameras are installed along the roads and road intersections where they're wont to collect traffic data. The info is then analyzed to get traffic parameters like road traffic density. This paper presents a comfortable and stylish approach for estimating the road traffic density during daytime using image processing and computer vision algorithms. The video data collected is first weakened into frames, which are then preprocessed during a series of steps. Finally, the vehicles are detected and extracted from the pictures and Density estimated. The traffic density is then obtained because of the number of vehicles per unit area of the road section. The proposed approach was implemented in MATLAB R2013a and average vehicle detection accuracy of 96.0% and 82.1% were achieved for fast-paced and slow-moving traffic scenes.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Incremental Multi-Feature Tensor Subspace Learning Based Smart Traffic Control System and Traffic Density Calculation Using Image Processing


    Beteiligte:
    Suseendran, G. (Autor:in) / Akila, D. (Autor:in) / Balaganesh, D. (Autor:in) / Elangovan, V.R. (Autor:in) / Vijayalakshmi, V. (Autor:in)


    Erscheinungsdatum :

    19.01.2021


    Format / Umfang :

    578937 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    IoT based Smart Traffic density Control using Image Processing

    Frank, Anilloy / Khamis Al Aamri, Yasser Salim / Zayegh, Amer | IEEE | 2019


    Density Control Smart Traffic Signal Using Image Processing

    Umair, Muhammad / Nadeem, Hanzala / Mir, Junaid | IEEE | 2024


    Density Based Traffic Control System Using Image Processing

    Prakash, Uthara E. / Vishnupriya, K.T / Thankappan, Athira et al. | IEEE | 2018


    Smart Traffic Light Control System Using Image Processing

    Banu, Asha S.M / Lakchida, Soundarya A S / Shanthini, V S et al. | IEEE | 2022