Faster Bounding Box Annotation for Object Detection in Indoor Scenes
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Yksityiskohdat
| Alkuperäiskieli | Englanti |
|---|---|
| Otsikko | 2018 7th European Workshop on Visual Information Processing (EUVIP) |
| Kustantaja | IEEE |
| ISBN (elektroninen) | 978-1-5386-6897-9 |
| ISBN (painettu) | 978-1-5386-6898-6 |
| DOI - pysyväislinkit | |
| Tila | Julkaistu - marraskuuta 2018 |
| OKM-julkaisutyyppi | A4 Artikkeli konferenssijulkaisussa |
| Tapahtuma | EUROPEAN WORKSHOP ON VISUAL INFORMATION PROCESSING - Kesto: 1 tammikuuta 1900 → … |
Julkaisusarja
| Nimi | |
|---|---|
| ISSN (elektroninen) | 2471-8963 |
Conference
| Conference | EUROPEAN WORKSHOP ON VISUAL INFORMATION PROCESSING |
|---|---|
| Ajanjakso | 1/01/00 → … |
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.