Semi-automatic learning framework combining object detection and background subtraction

Sugino Nicolas Alejandro, Tsubasa Minematsu, Atsushi Shimada, Takashi Shibata, Rin Ichiro Taniguchi, Eiji Kaneko, Hiroyoshi Miyano

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

Abstract

Public datasets used to train modern object detection models do not contain all the object classes appearing in real-world surveillance scenes. Even if they appear, they might be vastly different. Therefore, object detectors implemented in the real world must accommodate unknown objects and adapt to the scene. We implemented a framework that combines background subtraction and unknown object detection to improve the pretrained detector’s performance and apply human intervention to review the detected objects to minimize the latent risk of introducing wrongly labeled samples to the training. The proposed system enhanced the original YOLOv3 object detector performance in almost all the metrics analyzed, and managed to incorporate new classes without losing previous training information.

Original languageEnglish
Title of host publicationVISAPP
EditorsGiovanni Maria Farinella, Petia Radeva, Jose Braz
PublisherSciTePress
Pages96-106
Number of pages11
ISBN (Electronic)9789897584022
Publication statusPublished - Jan 1 2020
Event15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISIGRAPP 2020 - Valletta, Malta
Duration: Feb 27 2020Feb 29 2020

Publication series

NameVISIGRAPP 2020 - Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications
Volume5

Conference

Conference15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISIGRAPP 2020
CountryMalta
CityValletta
Period2/27/202/29/20

All Science Journal Classification (ASJC) codes

  • Computer Graphics and Computer-Aided Design
  • Computer Science Applications
  • Computer Vision and Pattern Recognition

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  • Cite this

    Alejandro, S. N., Minematsu, T., Shimada, A., Shibata, T., Taniguchi, R. I., Kaneko, E., & Miyano, H. (2020). Semi-automatic learning framework combining object detection and background subtraction. In G. M. Farinella, P. Radeva, & J. Braz (Eds.), VISAPP (pp. 96-106). (VISIGRAPP 2020 - Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications; Vol. 5). SciTePress.