Passenger Detection, Counting, and Action Recognition for Self-Driving Public Transport Vehicles
Shih-Feng Kao, Huei‐Yung Lin · 2021
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.