Background Subtraction Basedon Encoder-Decoder Structured CNN

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with host publication)peer-review

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Author(s)

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Detail(s)

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
Publication statusPublished - Nov 2019

Publication series

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

Conference

Title5th Asian Conference on Pattern Recognition
Location
PlaceNew Zealand
CityAuckland
Period26 - 29 November 2019

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.

Research Area(s)

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

Citation Format(s)

Background Subtraction Basedon Encoder-Decoder Structured CNN. / Wang, Jingming; Chan, Kwok Leung.
Pattern Recognition: 5th Asian Conference, ACPR 2019, Auckland, New Zealand, November 26–29, 2019, Revised Selected Papers, Part II. ed. / Shivakumara Palaiahnakote; Gabriella Sanniti di Baja; Liang Wang; Wei Qi Yan. Springer, Cham, 2019. p. 351-361 (Lecture Notes in Computer Science ; Vol. 12047).

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with host publication)peer-review