Faster Bounding Box Annotation for Object Detection in Indoor Scenes

Bishwo Adhikari, Jukka Peltomäki, Jussi Puura, Heikki Huttunen

    Tutkimustuotos: KonferenssiartikkeliScientificvertaisarvioitu

    1 Sitaatiot (Scopus)

    Abstrakti

    This paper proposes an approach for rapid bounding box annotation for object detection datasets. The procedure consists of two stages: The first step is to annotate a part of the dataset manually, and the second step proposes annotations for the remaining samples using a model trained with the first stage annotations. We experimentally study which first/second stage split minimizes to total workload. In addition, we introduce a new fully labeled object detection dataset collected from indoor scenes. Compared to other indoor datasets, our collection has more class categories, diverse backgrounds, lighting conditions, occlusions and high intra-class differences. We train deep learning based object detectors with a number of state-of-the-art models and compare them in terms of speed and accuracy. The fully annotated dataset is released freely available for the research community.
    AlkuperäiskieliEnglanti
    Otsikko2018 7th European Workshop on Visual Information Processing (EUVIP)
    KustantajaIEEE
    ISBN (elektroninen)978-1-5386-6897-9
    ISBN (painettu)978-1-5386-6898-6
    DOI - pysyväislinkit
    TilaJulkaistu - marrask. 2018
    OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
    TapahtumaEUROPEAN WORKSHOP ON VISUAL INFORMATION PROCESSING -
    Kesto: 1 tammik. 1900 → …

    Julkaisusarja

    Nimi
    ISSN (elektroninen)2471-8963

    Conference

    ConferenceEUROPEAN WORKSHOP ON VISUAL INFORMATION PROCESSING
    Ajanjakso1/01/00 → …

    Julkaisufoorumi-taso

    • Jufo-taso 1

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