Rethinking Background and Foreground in Deep Neural Network-Based Background Subtraction

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

1 Citation (Scopus)

Abstract

Recently, deep neural networks have demonstrated excellent performance in foreground segmentation tasks such as moving object detection and change detection tasks. Various types of neural networks have been proposed, however, the previous works mainly discuss the accuracy. Analytics of the neural networks is important to utilize them effectively and improve their performance. In this paper, we investigate a foreground segmentation network and background subtraction network. In our analysis, we discuss differences of behaviors of the two networks in specific scenes and feature distributions in each layer of a background subtraction network to investigate feature learning. In addition, we provide suggestions about the comparison with these networks.

Original languageEnglish
Title of host publication2020 IEEE International Conference on Image Processing, ICIP 2020 - Proceedings
PublisherIEEE Computer Society
Pages3229-3233
Number of pages5
ISBN (Electronic)9781728163956
DOIs
Publication statusPublished - Oct 2020
Event2020 IEEE International Conference on Image Processing, ICIP 2020 - Virtual, Abu Dhabi, United Arab Emirates
Duration: Sep 25 2020Sep 28 2020

Publication series

NameProceedings - International Conference on Image Processing, ICIP
Volume2020-October
ISSN (Print)1522-4880

Conference

Conference2020 IEEE International Conference on Image Processing, ICIP 2020
CountryUnited Arab Emirates
CityVirtual, Abu Dhabi
Period9/25/209/28/20

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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