A specific perspective: Subway driver behaviour recognition using CNN and time-series diagram

Urban rail transit, especially the subway, has been booming in China for a decade, imposing safety challenges on all related parties. Drivers’ behaviours are particularly crucial. Typically, drivers’ actions are recorded by cameras, and the surveillance videos are evaluated manually. Current driver behaviour recognition methods mostly target the bus or car drivers and can hardly be implemented for subways, because subway drivers follow a rigid working code that needs a time sequence of movements to describe. In this study, the authors propose a recognition model to automatically recognise behaviours from single-frame images that are extracted from surveillance videos; second, the authors convert the recognition results into time series diagrams, thus the recognised behaviours can be interpreted and analysed statistically and effectively. The validation experiments demonstrate that the convolutional neural network model can recognise 96.20% driver behaviours, and time series diagrams add time information to the behaviours, providing a convincible reference for subway driver evaluation.


  • English

Media Info

Subject/Index Terms

Filing Info

  • Accession Number: 01780435
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Aug 27 2021 2:56PM