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
    @inproceedings{0f4f648a9b3049fbbe983afefab8ba86,
    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",
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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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    AU - Martinkauppi, B.

    AU - Jokela, Maria

    AU - Kutila, Matti

    AU - Leino, Jarkko

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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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    KW - traffic flow surveillance

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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