The motive of this Traffic Police Hand Gesture Recognition System for self reliant cars is constructed the use of the MoveNet Thunder version from TensorFlow`s MoveNet. It locates 3 most important gestures regularly utilized by visitors police in India, namely `Stop', 'Turn Left', and 'Move Forward'. To boom robustness, the gadget is skilled o n a custom dataset of 8000 pix taken in special environments, and neural community structure at the full-size quantity of information include Dense and dropout layers to maximise accuracy and keep away from overfitting. Furthermore, actual time face detection is used, wherein the officer is best recorded if he's looking the camera, and Haar cascades are used for that. The version is capable of acquire 89% accuracy, sturdy category overall performance throughout maximum gesture training are observed; but mild misclassifications took place among comparable gestures and became demonstrated with the CARLA simulator below everyday and climate and lights various conditions. Promising outcomes are tested through the prototype for secure and green integration into the self reliant car navigation in visitors managed environments.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Enhancing Autonomous Vehicle Navigation: Traffic Police Hand Gesture Recognition for Self-Driving Cars in India using MoveNet Thunder


    Beteiligte:
    Malini, A Hema (Autor:in) / Babu, B Praveen (Autor:in) / Logesh, K S (Autor:in)


    Erscheinungsdatum :

    17.06.2025


    Format / Umfang :

    592731 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch






    Enhanced BiLSTM for Traffic Police Gesture Recognition in Autonomous Vehicles

    Sunil, Vaidya Pranay / Anjali, A. / Devangan, Deepak Kumar | IEEE | 2024