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 proceedingConference article in proceedingsScientificpeer-review

2 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 languageEnglish
Title of host publicationProceedings
Subtitle of host publicationIEEE 8th International Conference on Intelligent Computer Communication and Processing, ICCP 2012
PublisherIEEE Institute of Electrical and Electronic Engineers
Pages117-122
ISBN (Electronic)978-1-4673-2952-1
ISBN (Print)978-1-4673-2953-8
DOIs
Publication statusPublished - 2012
MoE publication typeNot Eligible
EventIEEE 8th International Conference on Intelligent Computer Communication and Processing, ICCP 2012 - Cluj-Napoca, Romania
Duration: 30 Aug 20121 Sep 2012

Conference

ConferenceIEEE 8th International Conference on Intelligent Computer Communication and Processing, ICCP 2012
Abbreviated titleICCP 2012
CountryRomania
CityCluj-Napoca
Period30/08/121/09/12

Fingerprint

Computer vision
Data fusion
Chemical sensors
Sensors

Keywords

  • camera surveillance
  • CO2
  • emission estimation
  • traffic emissions
  • traffic flow surveillance

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

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 proceedingConference article in proceedingsScientificpeer-review

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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.

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Pyykönen P, Martinkauppi B, Jokela M, Kutila M, Leino J. 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 . 2012. p. 117-122 https://doi.org/10.1109/ICCP.2012.6356173