Learning an object detector using zoomed object regions

Sung Jin Cho, Seung Wook Kim, Kwang Hyun Uhm, Hyong Keun Kook, Sung-Jea Ko

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The single shot multi-box detector (SSD) is one of the first real-time detectors, which uses a convolutional neural network (CNN) and achieves the state-of-the-art detection performance. However, owing to the semantic gap between each feature layer of CNN, the SSD has a room for improvement. In this paper, we propose a novel training scheme to enhance the performance of the SSD. In object detection, ground truth (GT) box is a bounding box enclosing an object boundary. To improve the semantic level of the feature map, we generate additional GT boxes by zooming in to and out from the original GT boxes. Experimental results show that the SSD trained with our scheme outperforms the original one on public dataset.

Original languageEnglish
Title of host publicationICEIC 2019 - International Conference on Electronics, Information, and Communication
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9788995004449
DOIs
Publication statusPublished - 2019 May 3
Event18th International Conference on Electronics, Information, and Communication, ICEIC 2019 - Auckland, New Zealand
Duration: 2019 Jan 222019 Jan 25

Publication series

NameICEIC 2019 - International Conference on Electronics, Information, and Communication

Conference

Conference18th International Conference on Electronics, Information, and Communication, ICEIC 2019
CountryNew Zealand
CityAuckland
Period19/1/2219/1/25

Keywords

  • Computer vision
  • Neural network
  • Object detection

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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  • Cite this

    Cho, S. J., Kim, S. W., Uhm, K. H., Kook, H. K., & Ko, S-J. (2019). Learning an object detector using zoomed object regions. In ICEIC 2019 - International Conference on Electronics, Information, and Communication [8706381] (ICEIC 2019 - International Conference on Electronics, Information, and Communication). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.23919/ELINFOCOM.2019.8706381