TY - GEN
T1 - Background model based on statistical local difference pattern
AU - Yoshinaga, Satoshi
AU - Shimada, Atsushi
AU - Nagahara, Hajime
AU - Taniguchi, Rin Ichiro
PY - 2013
Y1 - 2013
N2 - We present a robust background model for object detection and report its evaluation results using the database of Background Models Challenge (BMC). Our background model is based on a statistical local feature. In particular, we use an illumination invariant local feature and describe its distribution by using a statistical framework. Thanks to the effectiveness of the local feature and the statistical framework, our method can adapt to both illumination and dynamic background changes. Experimental results, which are done thanks to the database of BMC, show that our method can detect foreground objects robustly against background changes.
AB - We present a robust background model for object detection and report its evaluation results using the database of Background Models Challenge (BMC). Our background model is based on a statistical local feature. In particular, we use an illumination invariant local feature and describe its distribution by using a statistical framework. Thanks to the effectiveness of the local feature and the statistical framework, our method can adapt to both illumination and dynamic background changes. Experimental results, which are done thanks to the database of BMC, show that our method can detect foreground objects robustly against background changes.
UR - http://www.scopus.com/inward/record.url?scp=84876007783&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84876007783&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-37410-4_30
DO - 10.1007/978-3-642-37410-4_30
M3 - Conference contribution
AN - SCOPUS:84876007783
SN - 9783642374098
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 327
EP - 332
BT - Computer Vision - ACCV 2012 International Workshops, Revised Selected Papers
T2 - 11th Asian Conference on Computer Vision, ACCV 2012
Y2 - 5 November 2012 through 9 November 2012
ER -