Background Subtraction Basedon Encoder-Decoder Structured CNN

Jingming Wang, Kwok Leung Chan*

*Corresponding author for this work

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

1 Citation (Scopus)

Abstract

Background subtraction is commonly adopted for detecting moving objects in image sequence. It is an important and fundamental computer vision task and has a wide range of applications. We propose a background subtraction framework with deep learning model. Pixels are labeled as background or foreground by an Encoder-Decoder Structured Convolutional Neural Network (CNN). The encoder part produces a high-level feature vector. Then, the decoder part uses the feature vector to generate a binary segmentation map, which can be used to identify moving objects. The background model is generated from the image sequence. Each frame of the image sequence and the background model are input to the CNN for pixel classification. Background subtraction result can be erroneous as videos may be captured in various complex scenes. The background model must be updated. Therefore, we propose a feedback scheme to perform the pixelwise background model updating. For the training of the CNN, the input images and the corresponding ground truths are drawn from the benchmark dataset Change Detection 2014. The results show that our proposed architecture outperforms many well-known traditional and deep learning background subtraction algorithms.
Original languageEnglish
Title of host publicationPattern Recognition
Subtitle of host publication5th Asian Conference, ACPR 2019, Auckland, New Zealand, November 26–29, 2019, Revised Selected Papers, Part II
EditorsShivakumara Palaiahnakote, Gabriella Sanniti di Baja, Liang Wang, Wei Qi Yan
PublisherSpringer, Cham
Pages351-361
ISBN (Electronic)978-3-030-41299-9
ISBN (Print)978-3-030-41298-2
DOIs
Publication statusPublished - Nov 2019
Event5th Asian Conference on Pattern Recognition - Auckland, New Zealand
Duration: 26 Nov 201929 Nov 2019

Publication series

NameLecture Notes in Computer Science
Volume12047
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference5th Asian Conference on Pattern Recognition
Country/TerritoryNew Zealand
CityAuckland
Period26/11/1929/11/19

Research Keywords

  • Background modeling
  • Background subtraction
  • Convolutional neural network
  • Deep learning
  • Foreground detection

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