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.
|Number of pages||6|
|State||Published - 2018|
|Event||9th International Conference on Pattern Recognition Systems, ICPRS 2018 - Valparaiso, Chile|
Duration: 22 May 2018 → 24 May 2018
|Conference||9th International Conference on Pattern Recognition Systems, ICPRS 2018|
|Period||22/05/18 → 24/05/18|
Bibliographical noteFunding Information:
The authors gratefully acknowledge the Chilean National Science and Technology Council (Conicyt) for its funding under grants CONICYT-Fondecyt Regular nos. 1140209 (“OBSERVE”) , 1120219, and 1080381 . S.A. Velastin is grateful to funding received from the Universidad Carlos III de Madrid, the European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreement no. 600371, el Ministerio de Economía y Competitividad (COFUND2013-51509) and Banco Santander. Finally, we are grateful to NVIDIA for its donation as part of its academic GPU Grant Program.
© 2018 Institution of Engineering and Technology. All rights reserved.
- Deep Learning
- Pedestrian Detection
- Support Vector Machine