Pothole Detection and Tracking in Car Video Sequence

Ionut Schiopu, Jukka P. Saarinen, Lauri Kettunen, Ioan Tabus

    Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

    20 Citations (Scopus)
    348 Downloads (Pure)

    Abstract

    In this paper, we propose a low complexity method for detection and tracking of potholes in video sequences taken by a camera placed inside a moving car.
    The region of interest for the detection of the potholes is selected as the image area where the road is observed with the highest resolution. A threshold-based algorithm generates a set of candidate regions.
    For each region the following features are extracted: its size, the regularity of the intensity surface, contrast with respect to background model, and the region's contour length and shape. The candidate regions are labeled as putative potholes by a decision tree according to these features, eliminating the false positives due to shadows of wayside objects.
    The putative potholes that are successfully tracked in consecutive frames are finally declared potholes.
    Experimental results with real video sequences show a good detection precision.
    Original languageEnglish
    Title of host publicationInternational Conference on Telecommunications and Signal Processing (TSP)
    PublisherIEEE
    Pages701-706
    Number of pages6
    ISBN (Electronic)978-1-5090-1288-6
    ISBN (Print)978-1-5090-1287-9
    Publication statusPublished - Jun 2016
    Publication typeA4 Article in conference proceedings
    EventInternational Conference on Telecommunications and Signal Processing - , United Kingdom
    Duration: 1 Jan 2015 → …

    Conference

    ConferenceInternational Conference on Telecommunications and Signal Processing
    Country/TerritoryUnited Kingdom
    Period1/01/15 → …

    Keywords

    • pothole detection
    • pothole tracking
    • region of interest
    • Euclidean distance mapping

    Publication forum classification

    • Publication forum level 1

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