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

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

1 被引用数 (Scopus)

抄録

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.

本文言語英語
ホスト出版物のタイトル2020 IEEE International Conference on Image Processing, ICIP 2020 - Proceedings
出版社IEEE Computer Society
ページ3229-3233
ページ数5
ISBN(電子版)9781728163956
DOI
出版ステータス出版済み - 10 2020
イベント2020 IEEE International Conference on Image Processing, ICIP 2020 - Virtual, Abu Dhabi, アラブ首長国連邦
継続期間: 9 25 20209 28 2020

出版物シリーズ

名前Proceedings - International Conference on Image Processing, ICIP
2020-October
ISSN(印刷版)1522-4880

会議

会議2020 IEEE International Conference on Image Processing, ICIP 2020
Countryアラブ首長国連邦
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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