Non-linear learning factor control for statistical adaptive background subtraction algorithm

Thongkamwitoon, T.; Aramvith, S.; Chalidabhongse, T. H.

doi:10.1109/ISCAS.2005.1465454

Non-linear learning factor control for statistical adaptive background subtraction algorithm

Author(s)

Thongkamwitoon, T.

Aramvith, S.

Chalidabhongse, T. H.

Date Issued

December 1, 2005

Type

Conference Paper

DOI

10.1109/ISCAS.2005.1465454

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. © 2005 IEEE.

Citation

Proceedings IEEE International Symposium on Circuits and Systems, 3785-3788, 2005