Detection of people boarding/alighting a metropolitan train using computer vision

M. Belloc, S. A. Velastin, R. Fernandez, M. Jara

Resultado de la investigación: Contribución a una conferenciaArtículo

Resumen

All rights reserved. Pedestrian detection and tracking have seen a major progress in the last two decades. Nevertheless there are always application areas which either require further improvement or that have not been sufficiently explored or where production level performance (accuracy and computing efficiency) has not been demonstrated. One such area is that of pedestrian monitoring and counting in metropolitan railways platforms. In this paper we first present a new partly annotated dataset of a full-size laboratory observation of people boarding and alighting from a public transport vehicle. We then present baseline results for automatic detection of such passengers, based on computer vision, that could open the way to compute variables of interest to traffic engineers and vehicle designers such as counts and flows and how they are related to vehicle and platform layout.
Idioma originalInglés estadounidense
Páginas22-27
Número de páginas6
EstadoPublicada - 1 ene 2018
EventoIET Conference Publications -
Duración: 1 ene 2018 → …

Conferencia

ConferenciaIET Conference Publications
Período1/01/18 → …

Palabras clave

  • Deep Learning
  • HOG
  • Pedestrian Detection
  • Support Vector Machine

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