Background subtraction using encoder-decoder structured convolutional neural network

Kyungsun Lim, Won Dong Jang, Chang-Su Kim

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

    65 Citations (Scopus)

    Abstract

    A background subtraction algorithm using an encoderdecoder structured convolutional neural network is proposed in this work, in order to segment out moving objects from the background. A target frame, its previous frame, and a background model are concatenated and fed into the network as the input. Then, the encoder generates a highlevel feature vector, and the decoder converts the feature vector into a segmentation map, which roughly identifies moving object regions. Moreover, we develop background modeling and foreground extraction techniques, which exploit contour information. Experimental results on the CD-net2014 dataset demonstrate that the proposed algorithm outperforms state-of-the-art techniques significantly.

    Original languageEnglish
    Title of host publication2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2017
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    ISBN (Electronic)9781538629390
    DOIs
    Publication statusPublished - 2017 Oct 20
    Event14th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2017 - Lecce, Italy
    Duration: 2017 Aug 292017 Sept 1

    Publication series

    Name2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2017

    Other

    Other14th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2017
    Country/TerritoryItaly
    CityLecce
    Period17/8/2917/9/1

    Bibliographical note

    Funding Information:
    This work was supported partly by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (No. NRF2015R1A2A1A10055037), and partly by the Agency for Defense Development (ADD) and Defense Acquisition Program Administration (DAPA) of Korea (UC160016FD).

    Publisher Copyright:
    © 2017 IEEE.

    ASJC Scopus subject areas

    • Computer Vision and Pattern Recognition
    • Signal Processing

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