TY - GEN
T1 - Background initialization based on bidirectional analysis and consensus voting
AU - Minematsu, Tsubasa
AU - Shimada, Atsushi
AU - Taniguchi, Rin Ichiro
N1 - Funding Information:
This work was partially supported by JSPS KAKENHI Grant-in-Aid for Challenging Exploratory Research No.25540072, No.15K12066 and Grant-in-Aid for JSPS Fellows No.16J02614.
Publisher Copyright:
© 2016 IEEE.
PY - 2016/1/1
Y1 - 2016/1/1
N2 - Background modeling and subtraction are essential to video surveillance applications. There are two main issues related to background modeling: how to initialize the background model, and how to update the model based on observations. In this paper, we consider the first issue with the aim of generating a clear background image that does not contain foreground objects or noise. We used a bidirectional analysis and consensus voting strategy to achieve this goal. We demonstrated the effectiveness of our technique using open access datasets.
AB - Background modeling and subtraction are essential to video surveillance applications. There are two main issues related to background modeling: how to initialize the background model, and how to update the model based on observations. In this paper, we consider the first issue with the aim of generating a clear background image that does not contain foreground objects or noise. We used a bidirectional analysis and consensus voting strategy to achieve this goal. We demonstrated the effectiveness of our technique using open access datasets.
UR - http://www.scopus.com/inward/record.url?scp=85019068333&partnerID=8YFLogxK
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U2 - 10.1109/ICPR.2016.7899620
DO - 10.1109/ICPR.2016.7899620
M3 - Conference contribution
AN - SCOPUS:85019068333
T3 - Proceedings - International Conference on Pattern Recognition
SP - 126
EP - 131
BT - 2016 23rd International Conference on Pattern Recognition, ICPR 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 23rd International Conference on Pattern Recognition, ICPR 2016
Y2 - 4 December 2016 through 8 December 2016
ER -