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.
|Número de páginas||6|
|Estado||Publicada - 2018|
|Evento||9th International Conference on Pattern Recognition Systems, ICPRS 2018 - Valparaiso, Chile|
Duración: 22 may 2018 → 24 may 2018
|Conferencia||9th International Conference on Pattern Recognition Systems, ICPRS 2018|
|Período||22/05/18 → 24/05/18|
Nota bibliográficaPublisher Copyright:
© 2018 Institution of Engineering and Technology. All rights reserved.
- Deep Learning
- Pedestrian Detection
- Support Vector Machine