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Adaptive parametric statistical background subtraction for video segmentation
Author(s)
Amnuaykanchanasin, P.
Thongkamwitoon, T.
Srisawaiwilai, N.
Aramvith, S.
Chalidabhongse, T. H.
Date Issued
November 11, 2005
Type
Conference Paper
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.
Citation
Vssn 2005 Proceedings of the 3rd ACM International Workshop on Video Surveillance and Sensor Networks Co Located with ACM Multimedia 2005, 63-66, 2005