TY - GEN
T1 - Background subtraction using encoder-decoder structured convolutional neural network
AU - Lim, Kyungsun
AU - Jang, Won Dong
AU - Kim, Chang-Su
N1 - 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.
PY - 2017/10/20
Y1 - 2017/10/20
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85039918235&partnerID=8YFLogxK
U2 - 10.1109/AVSS.2017.8078547
DO - 10.1109/AVSS.2017.8078547
M3 - Conference contribution
AN - SCOPUS:85039918235
T3 - 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2017
BT - 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 14th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2017
Y2 - 29 August 2017 through 1 September 2017
ER -