Exploiting temporal and spatial constraints in traffic sign detection from a moving vehicle

Sinisa Segvic, Karla Brkic, Zoran Kalafatic, Axel Pinz

    Research output: Contribution to journalArticlepeer-review


    This paper addresses detection, tracking and recognition of traffic signs in video. Previous research has shown that very good detection recalls can be ob-
    tained by state-of-the-art detection algorithms. Unfortunately, satisfactory precision and localization accuracy are more difficultly achieved. We follow the intuitive notion that it should be easier to accurately detect an object from an image sequence than from a single image. We propose a novel two-stage technique which achieves improved detection results by applying temporal and spatial constraints to the occurrences of traffic signs in video. The first stage produces well-aligned temporally consistent detection tracks, by managing
    many competing track hypotheses at once. The second stage improves the precision by filtering the detection tracks by a learned discriminative model. The two stages have been evaluated in extensive experiments performed on videos acquired from a moving vehicle. The obtained experimental results clearly confirm the advantages of the proposed technique.
    Original languageEnglish
    Pages (from-to)649–665
    JournalMachine vision and applications
    Issue number3
    Publication statusPublished - 2014


    • Video analysis
    • Object detection
    • object tracking
    • discriminative models
    • supervised learning

    Fields of Expertise

    • Information, Communication & Computing

    Treatment code (Nähere Zuordnung)

    • Basic - Fundamental (Grundlagenforschung)
    • Application


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