Visual Vehicle Egomotion Estimation using the Fourier-Mellin Transform

Roland Goecke, Arkshay Asthana, Niklas Pettersson, Lars Petersson

    Research output: A Conference proceeding or a Chapter in BookConference contribution

    31 Citations (Scopus)

    Abstract

    This paper is concerned with the problem of estimating the motion of a single camera from a sequence of images, with an application scenario of vehicle egomotion estimation. Egomotion estimation has been an active area of research for many years and various solutions to the problem have been proposed. Many methods rely on optical flow or local image features to establish the spatial relationship between two images. A new method of egomotion estimation is presented which makes use of the Fourier-Mellin Transform for registering images in a video sequence, from which the rotation and translation of the camera motion can be estimated. The Fourier-Mellin Transform provides an accurate and efficient way of computing the camera motion parameters. It is a global method that takes the contributions from all pixels into account. The performance of the proposed approach is compared to two variants of optical flow methods and results are presented for a real-world video sequence taken from a moving vehicle
    Original languageEnglish
    Title of host publicationProceedings of the 2007 IEEE Intelligent Vehicles Symposium IV
    EditorsUrbano Nunes
    Place of PublicationAustralia
    PublisherIEEE, Institute of Electrical and Electronics Engineers
    Pages450-455
    Number of pages6
    ISBN (Print)9781424410675
    DOIs
    Publication statusPublished - 2007
    Event2007 IEEE Intelligent Vehicles Symposium IV - Istanbul, Istanbul, Turkey
    Duration: 13 Jun 200715 Jun 2007

    Conference

    Conference2007 IEEE Intelligent Vehicles Symposium IV
    CountryTurkey
    CityIstanbul
    Period13/06/0715/06/07

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    Cite this

    Goecke, R., Asthana, A., Pettersson, N., & Petersson, L. (2007). Visual Vehicle Egomotion Estimation using the Fourier-Mellin Transform. In U. Nunes (Ed.), Proceedings of the 2007 IEEE Intelligent Vehicles Symposium IV (pp. 450-455). Australia: IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/IVS.2007.4290156