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

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

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

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

Research output: Contribution to conference › Paper › peer-review

4 Scopus citations

Abstract

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.

Original language	English
Pages	22-27
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

Conference	9th International Conference on Pattern Recognition Systems, ICPRS 2018
Country/Territory	Chile
City	Valparaiso
Period	22/05/18 → 24/05/18

Bibliographical note

Funding 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.

Publisher Copyright:
© 2018 Institution of Engineering and Technology. All rights reserved.

Keywords

Deep Learning
HOG
Pedestrian Detection
Support Vector Machine

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Cite this

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title = "Detection of people boarding/alighting a metropolitan train using computer vision",

abstract = "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.",

keywords = "Deep Learning, HOG, Pedestrian Detection, Support Vector Machine, Deep Learning, HOG, Pedestrian Detection, Support Vector Machine",

author = "M. Belloc and Velastin, {S. A.} and R. Fernandez and M. Jara",

note = "Funding 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{\textquoteright}s Seventh Framework Programme for research, technological development and demonstration under grant agreement no. 600371, el Ministerio de Econom{\'i}a y Competitividad (COFUND2013-51509) and Banco Santander. Finally, we are grateful to NVIDIA for its donation as part of its academic GPU Grant Program. Publisher Copyright: {\textcopyright} 2018 Institution of Engineering and Technology. All rights reserved.; 9th International Conference on Pattern Recognition Systems, ICPRS 2018 ; Conference date: 22-05-2018 Through 24-05-2018",

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language = "English",

pages = "22--27",

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AU - Velastin, S. A.

AU - Fernandez, R.

AU - Jara, M.

N1 - Funding 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. Publisher Copyright: © 2018 Institution of Engineering and Technology. All rights reserved.

PY - 2018

Y1 - 2018

N2 - 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.

AB - 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.

KW - Deep Learning

KW - HOG

KW - Pedestrian Detection

KW - Support Vector Machine

KW - Deep Learning

KW - HOG

KW - Pedestrian Detection

KW - Support Vector Machine

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M3 - Paper

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T2 - 9th International Conference on Pattern Recognition Systems, ICPRS 2018

Y2 - 22 May 2018 through 24 May 2018

ER -

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

Abstract

Conference

Bibliographical note

Keywords

UN SDGs

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Cite this