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Faster Bounding Box Annotation for Object Detection in Indoor Scenes

Tutkimustuotosvertaisarvioitu

Standard

Faster Bounding Box Annotation for Object Detection in Indoor Scenes. / Adhikari, Bishwo; Peltomäki, Jukka; Puura, Jussi; Huttunen, Heikki.

2018 7th European Workshop on Visual Information Processing (EUVIP). IEEE, 2018.

Tutkimustuotosvertaisarvioitu

Harvard

Adhikari, B, Peltomäki, J, Puura, J & Huttunen, H 2018, Faster Bounding Box Annotation for Object Detection in Indoor Scenes. julkaisussa 2018 7th European Workshop on Visual Information Processing (EUVIP). IEEE, EUROPEAN WORKSHOP ON VISUAL INFORMATION PROCESSING, 1/01/00. https://doi.org/10.1109/EUVIP.2018.8611732

APA

Adhikari, B., Peltomäki, J., Puura, J., & Huttunen, H. (2018). Faster Bounding Box Annotation for Object Detection in Indoor Scenes. teoksessa 2018 7th European Workshop on Visual Information Processing (EUVIP) IEEE. https://doi.org/10.1109/EUVIP.2018.8611732

Vancouver

Adhikari B, Peltomäki J, Puura J, Huttunen H. Faster Bounding Box Annotation for Object Detection in Indoor Scenes. julkaisussa 2018 7th European Workshop on Visual Information Processing (EUVIP). IEEE. 2018 https://doi.org/10.1109/EUVIP.2018.8611732

Author

Adhikari, Bishwo ; Peltomäki, Jukka ; Puura, Jussi ; Huttunen, Heikki. / Faster Bounding Box Annotation for Object Detection in Indoor Scenes. 2018 7th European Workshop on Visual Information Processing (EUVIP). IEEE, 2018.

Bibtex - Lataa

@inproceedings{f9444cdf5b494251a1fa80ffd87e9973,
title = "Faster Bounding Box Annotation for Object Detection in Indoor Scenes",
abstract = "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.",
author = "Bishwo Adhikari and Jukka Peltom{\"a}ki and Jussi Puura and Heikki Huttunen",
year = "2018",
month = "11",
doi = "10.1109/EUVIP.2018.8611732",
language = "English",
isbn = "978-1-5386-6898-6",
publisher = "IEEE",
booktitle = "2018 7th European Workshop on Visual Information Processing (EUVIP)",

}

RIS (suitable for import to EndNote) - Lataa

TY - GEN

T1 - Faster Bounding Box Annotation for Object Detection in Indoor Scenes

AU - Adhikari, Bishwo

AU - Peltomäki, Jukka

AU - Puura, Jussi

AU - Huttunen, Heikki

PY - 2018/11

Y1 - 2018/11

N2 - 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.

AB - 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.

U2 - 10.1109/EUVIP.2018.8611732

DO - 10.1109/EUVIP.2018.8611732

M3 - Conference contribution

SN - 978-1-5386-6898-6

BT - 2018 7th European Workshop on Visual Information Processing (EUVIP)

PB - IEEE

ER -