Multiple object tracking for robust quantitative analysis of passenger motion while boarding and alighting a metropolitan Train

Jose Sebastian Gomez Meza, Jose Delpiano, Sergio A. Velastin, Rodrigo Fernandez, Sebastian Seriani Awad

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

Resumen

To achieve significant improvements in public transport it is necessary to develop an autonomous system that locates and counts passengers in real time in scenarios with a high level of occlusion, providing tools to efficiently solve problems such as reduction and stabilization in travel times, greater fluency, better control of fleets and less congestion. A deep learning method based in transfer learning is used to accomplish this: You Only Look Once (YOLO) version 3 and Faster RCNN Inception version 2 architectures are fine tuned using PAMELA-UANDES dataset, which contains annotated images of the boarding and alighting of passengers on a subway platform from a superior perspective. The locations given by the detector are passed through a multiple object tracking system implemented based on a Markov decision process that associates subjects in consecutive frames and assigns identities considering overlaps between past detections and predicted positions using a Kalman filter.

Idioma originalInglés
Título de la publicación alojadaIET Conference Proceedings
EditorialInstitution of Engineering and Technology
Páginas231-238
Número de páginas8
Volumen2021
Edición1
ISBN (versión digital)9781839534300, 9781839535048, 9781839535741, 9781839535918, 9781839536045, 9781839536052, 9781839536069, 9781839536199, 9781839536366, 9781839536588, 9781839536793, 9781839536809, 9781839536816, 9781839536847, 9781839537035
DOI
EstadoPublicada - 2021
Evento11th International Conference of Pattern Recognition Systems, ICPRS 2021 - Virtual, Online
Duración: 17 mar. 202119 mar. 2021

Conferencia

Conferencia11th International Conference of Pattern Recognition Systems, ICPRS 2021
CiudadVirtual, Online
Período17/03/2119/03/21

Nota bibliográfica

Publisher Copyright:
© 2021 IET Conference Proceedings. All rights reserved.

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