16 Jun Student | Julien Collart | Design, Modelling and Simulation
University of Technology Sydney (Supervisor: Alen Alempijevic)
PhD topic – Integrated passenger behaviour, train operations diagnostics and vehicle condition monitoring system
The University of Technology Sydney, together with Downer, created a complex dwell-time diagnostics tool that uses 3D Infrared cameras and algorithms, to anonymously monitor passenger numbers and movement at train station platforms. Named Dwell Track, the technology can quantify passenger congestion on platforms and near train doors that can lead to extended dwell times. Data from Dwell Track could enable train operators to improve platform management procedures and communications to passengers.
Expected completion date
- Proposed an approach for detecting the dwell-events of a train during its journey through the train station relying on depth images.
- Provided a framework for postures and gait phases (motion states) prediction using solely the upper-body motion extracted from privacy-friendly data.
- Provided a solution for improving the reliability of human tracking in public and often crowded environments by leveraging the motion states’ prediction while using limited noisy features.
Collart, J., Fitch, R. and Alempijevic, A. Motion States Inference through 3D Shoulder Gait Analysis and Hierarchical Hidden Markov Models. Australasian Conference on Robotics and Automation 2017, 2017, pp. 1 – 8
Collart, J., Kirchner, N., Alempijevic, A. and Zeibots, M. Foundation technology for development of an autonomous complex dwell time diagnostics tool, Proceedings of the Australasian Transport Research Forum 2015.