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 language | English |
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Title of host publication | 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2017 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
ISBN (Electronic) | 9781538629390 |
DOIs | |
Publication status | Published - 2017 Oct 20 |
Event | 14th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2017 - Lecce, Italy Duration: 2017 Aug 29 → 2017 Sept 1 |
Publication series
Name | 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2017 |
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Other
Other | 14th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2017 |
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Country/Territory | Italy |
City | Lecce |
Period | 17/8/29 → 17/9/1 |
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