A machine vision based working traffic emission estimation and surveillance schema

Pasi Pyykönen; B. Martinkauppi; Maria Jokela; Matti Kutila; Jarkko Leino

doi:10.1109/ICCP.2012.6356173

A machine vision based working traffic emission estimation and surveillance schema

Pasi Pyykönen, B. Martinkauppi, Maria Jokela, Matti Kutila, Jarkko Leino

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

3 Citations (Scopus)

Abstract

This paper suggest a new schema for improving accuracy of estimation of traffic CO2 emissions. The emission estimation is implemented as a part of novel traffic surveillance system which is movable and uses data fusion of several sensors and databases. The system is able to determine the emissions in real-time based on the traffic flow observed and this provides advantages over the current methods. The emissions are often approximated by using estimates of average traffic flow and emission rates but this produces very unreliable results. Another way is to use gas sensors but they are expensive and provide only point measurement data. In this paper, we show the feasibility of the novel schema.

Original language	English
Title of host publication	Proceedings
Subtitle of host publication	IEEE 8th International Conference on Intelligent Computer Communication and Processing, ICCP 2012
Publisher	IEEE Institute of Electrical and Electronic Engineers
Pages	117-122
ISBN (Electronic)	978-1-4673-2952-1
ISBN (Print)	978-1-4673-2953-8
DOIs	https://doi.org/10.1109/ICCP.2012.6356173
Publication status	Published - 2012
MoE publication type	Not Eligible
Event	IEEE 8th International Conference on Intelligent Computer Communication and Processing, ICCP 2012 - Cluj-Napoca, Romania Duration: 30 Aug 2012 → 1 Sept 2012

Conference

Conference	IEEE 8th International Conference on Intelligent Computer Communication and Processing, ICCP 2012
Abbreviated title	ICCP 2012
Country/Territory	Romania
City	Cluj-Napoca
Period	30/08/12 → 1/09/12

Keywords

camera surveillance
CO2
emission estimation
traffic emissions
traffic flow surveillance

UN SDGs

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

Access to Document

10.1109/ICCP.2012.6356173

Cite this

Pyykönen, P., Martinkauppi, B., Jokela, M., Kutila, M., & Leino, J. (2012). A machine vision based working traffic emission estimation and surveillance schema. In Proceedings: IEEE 8th International Conference on Intelligent Computer Communication and Processing, ICCP 2012 (pp. 117-122). IEEE Institute of Electrical and Electronic Engineers. https://doi.org/10.1109/ICCP.2012.6356173

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title = "A machine vision based working traffic emission estimation and surveillance schema",

abstract = "This paper suggest a new schema for improving accuracy of estimation of traffic CO2 emissions. The emission estimation is implemented as a part of novel traffic surveillance system which is movable and uses data fusion of several sensors and databases. The system is able to determine the emissions in real-time based on the traffic flow observed and this provides advantages over the current methods. The emissions are often approximated by using estimates of average traffic flow and emission rates but this produces very unreliable results. Another way is to use gas sensors but they are expensive and provide only point measurement data. In this paper, we show the feasibility of the novel schema.",

keywords = "camera surveillance, CO2, emission estimation, traffic emissions, traffic flow surveillance",

author = "Pasi Pyyk{\"o}nen and B. Martinkauppi and Maria Jokela and Matti Kutila and Jarkko Leino",

year = "2012",

doi = "10.1109/ICCP.2012.6356173",

language = "English",

isbn = "978-1-4673-2953-8",

pages = "117--122",

booktitle = "Proceedings",

publisher = "IEEE Institute of Electrical and Electronic Engineers",

address = "United States",

note = "IEEE 8th International Conference on Intelligent Computer Communication and Processing, ICCP 2012, ICCP 2012 ; Conference date: 30-08-2012 Through 01-09-2012",

}

Pyykönen, P, Martinkauppi, B, Jokela, M , Kutila, M & Leino, J 2012, A machine vision based working traffic emission estimation and surveillance schema. in Proceedings: IEEE 8th International Conference on Intelligent Computer Communication and Processing, ICCP 2012. IEEE Institute of Electrical and Electronic Engineers, pp. 117-122, IEEE 8th International Conference on Intelligent Computer Communication and Processing, ICCP 2012, Cluj-Napoca, Romania, 30/08/12. https://doi.org/10.1109/ICCP.2012.6356173

A machine vision based working traffic emission estimation and surveillance schema. / Pyykönen, Pasi; Martinkauppi, B.; Jokela, Maria et al.
Proceedings: IEEE 8th International Conference on Intelligent Computer Communication and Processing, ICCP 2012. IEEE Institute of Electrical and Electronic Engineers, 2012. p. 117-122.

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

TY - GEN

T1 - A machine vision based working traffic emission estimation and surveillance schema

AU - Pyykönen, Pasi

AU - Martinkauppi, B.

AU - Jokela, Maria

AU - Kutila, Matti

AU - Leino, Jarkko

PY - 2012

Y1 - 2012

N2 - This paper suggest a new schema for improving accuracy of estimation of traffic CO2 emissions. The emission estimation is implemented as a part of novel traffic surveillance system which is movable and uses data fusion of several sensors and databases. The system is able to determine the emissions in real-time based on the traffic flow observed and this provides advantages over the current methods. The emissions are often approximated by using estimates of average traffic flow and emission rates but this produces very unreliable results. Another way is to use gas sensors but they are expensive and provide only point measurement data. In this paper, we show the feasibility of the novel schema.

AB - This paper suggest a new schema for improving accuracy of estimation of traffic CO2 emissions. The emission estimation is implemented as a part of novel traffic surveillance system which is movable and uses data fusion of several sensors and databases. The system is able to determine the emissions in real-time based on the traffic flow observed and this provides advantages over the current methods. The emissions are often approximated by using estimates of average traffic flow and emission rates but this produces very unreliable results. Another way is to use gas sensors but they are expensive and provide only point measurement data. In this paper, we show the feasibility of the novel schema.

KW - camera surveillance

KW - CO2

KW - emission estimation

KW - traffic emissions

KW - traffic flow surveillance

U2 - 10.1109/ICCP.2012.6356173

DO - 10.1109/ICCP.2012.6356173

M3 - Conference article in proceedings

SN - 978-1-4673-2953-8

SP - 117

EP - 122

BT - Proceedings

PB - IEEE Institute of Electrical and Electronic Engineers

T2 - IEEE 8th International Conference on Intelligent Computer Communication and Processing, ICCP 2012

Y2 - 30 August 2012 through 1 September 2012

ER -

A machine vision based working traffic emission estimation and surveillance schema

Abstract

Conference

Keywords

UN SDGs

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Cite this