Robust foreground extraction technique using Gaussian family model and multiple thresholds

Authors:
Hansung Kim;Ryuuki Sakamoto;Itaru Kitahara;Tomoji Toriyama;Kiyoshi Kogure
Affiliations:
Knowledge Science Lab, ATR, Kyoto, Japan;Knowledge Science Lab, ATR, Kyoto, Japan;Knowledge Science Lab, ATR, Kyoto, Japan and Dept. of Intelligent Interaction Technologies, Univ. of Tsukuba, Japan;Knowledge Science Lab, ATR, Kyoto, Japan;Knowledge Science Lab, ATR, Kyoto, Japan
Venue:
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part I
Year:
2007

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Abstract

We propose a robust method to extract silhouettes of foreground objects from color video sequences. To cope with various changes in the background, the background is modeled as generalized Gaussian Family of distributions and updated by the selective running average and static pixel observation. All pixels in the input video image are classified into four initial regions using background subtraction with multiple thresholds, after which shadow regions are eliminated using color components. The final foreground silhouette is extracted by refining the initial region using morphological processes. We have verified that the proposed algorithm works very well in various background and foreground situations through experiments.