DocumentCode :
3546820
Title :
Non-linear learning factor control for statistical adaptive background subtraction algorithm
Author :
Thongkamwitoon, T. ; Aramvith, Supavadee ; Chalidabhongse, T.H.
Author_Institution :
Dept. of Electr. Eng., Chulalongkorn Univ., Bangkok, Thailand
fYear :
2005
fDate :
23-26 May 2005
Firstpage :
3785
Abstract :
The background subtraction algorithm has been proven to be a very effective technique for automated video surveillance applications. In statistical approach, background model is usually estimated using Gaussian model and is adaptively updated to deal with changes in dynamic scene environment. However, most algorithms update background parameters linearly. As a result, the classification results are erroneous when performing background convergence process. In this paper, we present a novel learning factor control for adaptive background subtraction algorithm. The method adaptively adjusts the rate of adaptation in background model corresponding to events in video sequence. Experimental results show the algorithm improves classification accuracy compared to other known methods.
Keywords :
adaptive signal processing; convergence; image classification; image sequences; learning (artificial intelligence); natural scenes; security; statistical analysis; surveillance; video signal processing; Gaussian model; adaptation rate; adaptively updated background model; automated video surveillance applications; background convergence process; background model estimation; classification accuracy; erroneous classification; linearly updated background parameters; nonlinear learning factor control; statistical adaptive background subtraction algorithm; statistical approach; video sequence events; Adaptation model; Adaptive control; Classification algorithms; Computer vision; Convergence; Information technology; Layout; Programmable control; Video sequences; Video surveillance;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Circuits and Systems, 2005. ISCAS 2005. IEEE International Symposium on
Print_ISBN :
0-7803-8834-8
Type :
conf
DOI :
10.1109/ISCAS.2005.1465454
Filename :
1465454
Link To Document :
بازگشت