A sparse error compensation based incremental principal component analysis method for foreground detection

Ming Qin, Yao Lu*, Huijun Di, Tianfei Zhou

*此作品的通讯作者

科研成果: 期刊稿件会议文章同行评审

摘要

Foreground detection is a fundamental task in video processing. Recently, many background subspace estimation based foreground detection methods have been proposed. In this paper, a sparse error compensation based incremental principal component analysis method, which robustly updates background subspace and estimates foreground, is proposed for foreground detection. There are mainly two notable features in our method. First, a sparse error compensation process via a probability sampling procedure is designed for subspace updating, which reduces the interference of undesirable foreground signal. Second, the proposed foreground detection method could operate without an initial background subspace estimation, which enlarges the application scope of our method. Extensive experiments on multiple real video sequences show the superiority of our method.

源语言英语
页(从-至)233-242
页数10
期刊Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
9314
DOI
出版状态已出版 - 2015
活动16th Pacific-Rim Conference on Multimedia, PCM 2015 - Gwangju, 韩国
期限: 16 9月 201518 9月 2015

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