In the Vehicular Ad hoc Networks (VANET) environment, recognizing traffic accident events in the driving videos captured by vehicle-mounted cameras is an essential task. Generally, traffic accidents have a short duration in driving videos, and the backgrounds of driving videos are dynamic and complex. These make traffic accident detection quite challenging. To effectively and efficiently detect accidents from the driving videos, we propose an accident detection approach based on spatio–temporal feature encoding with a multilayer neural network. Specifically, the multilayer neural network is used to encode the temporal features of video for clustering the video frames. From the obtained frame clusters, we detect the border frames as the potential accident frames. Then, we capture and encode the spatial relationships of the objects detected from these potential accident frames to confirm whether these frames are accident frames. The extensive experiments demonstrate that the proposed approach achieves promising detection accuracy and efficiency for traffic accident detection, and meets the real-time detection requirement in the VANET environment.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Spatio-Temporal Feature Encoding for Traffic Accident Detection in VANET Environment


    Beteiligte:
    Zhou, Zhili (Autor:in) / Dong, Xiaohua (Autor:in) / Li, Zhetao (Autor:in) / Yu, Keping (Autor:in) / Ding, Chun (Autor:in) / Yang, Yimin (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    2022-10-01


    Format / Umfang :

    2169001 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Han Ning Highway Traffic Accident Spatio-Temporal Analysis

    Wang, Chen ;Du, Kai ;Jin, Yin Li | Trans Tech Publications | 2011


    Han Ning Highway Traffic Accident Spatio-Temporal Analysis

    Wang, Chen / Du, Kai / Jin, Yin-Li et al. | Tema Archiv | 2011


    Deep representation of imbalanced spatio‐temporal traffic flow data for traffic accident detection

    Mehrannia, Pouya / Bagi, Shayan Shirahmad Gale / Moshiri, Behzad et al. | Wiley | 2023

    Freier Zugriff

    Deep representation of imbalanced spatio‐temporal traffic flow data for traffic accident detection

    Pouya Mehrannia / Shayan Shirahmad Gale Bagi / Behzad Moshiri et al. | DOAJ | 2023

    Freier Zugriff

    A Real-Time Fuzzy Logic Based Accident Detection System in VANET Environment

    Mohanty, Anita / Mohanty, Subrat Kumar / Jena, Bhagyalaxmi | Springer Verlag | 2021