Robust Foreground Segmentation Based on Two Effective Background Models
Date
2008
Authors
Li, X.
Hu, W.
Zhang, Z.
Zhang, X.
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Conference paper
Citation
MM’08 : Proceedings of the 2008 ACM International Conference on Multimedia, with co-located Symposium & Workshops Vancouver, BC, Canada, October 27–31, 2008 / pp.223-228
Statement of Responsibility
Xi Li, Weiming Hu, Zhongfei Zhang, Xiaoqin Zhang
Conference Name
ACM International Conference on Multimedia Information Retrieval (1st : 2008 : Vancouver, Canada)
Abstract
Foreground segmentation is a common foundation for many computer vision applications such as tracking and behavior analysis. Most existing algorithms for foreground segmentation learn pixel-based statistical models, which are sensitive to dynamic scenes such as illumination change, shadow movement, and swaying trees. In order to address this problem, we propose two block-based background models using the recently developed incremental rank-(R1, R2, R3) tensor-based subspace learning algorithm (referred to as IRTSA [1]). These two IRTSA-based background models (i.e., IRTSAGBM and IRTSA-CBM respectively for grayscale and color images) incrementally learn low-order tensor-based eigenspace representations to fully capture the intrinsic spatio-temporal characteristics of a scene, leading to robust foreground segmentation results. Theoretic analysis and experimental evaluations demonstrate the promise and effectiveness of the proposed background models.
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Copyright 2008 ACM