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TUTCRIS

Faster Bounding Box Annotation for Object Detection in Indoor Scenes

Tutkimustuotosvertaisarvioitu

Yksityiskohdat

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 - marraskuuta 2018
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaEUROPEAN WORKSHOP ON VISUAL INFORMATION PROCESSING -
Kesto: 1 tammikuuta 1900 → …

Julkaisusarja

Nimi
ISSN (elektroninen)2471-8963

Conference

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

Tiivistelmä

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.

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