Due to the recent progress on autonomous driving, some technologies have been gradually deployed to the public transport vehicles. The passenger safety under the unmanned operating environment has become an emerging issue which requires much more attention. This paper presents a method for passenger detection, counting and action recognition inside a minibus. A top-view camera system is mounted on the ceiling to have a full coverage of the interior. 2D and 3D convolutional neural networks are developed for pose recognition and action classification of the passengers. The experiments are carried out in a self-driving minibus, and the results have demonstrated the feasibility of the proposed technique.


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    Title :

    Passenger Detection, Counting, and Action Recognition for Self-Driving Public Transport Vehicles


    Contributors:


    Publication date :

    2021-07-11


    Size :

    2857271 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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