Background Initialization Based on Adaptive Online Low-rank Subspace Learning

Background initialization is to estimate an appropriate representation for background of a scene, and has a decisive role in determining the performance of background subtraction. Background initialization based on low-rank subspace learning can obtain the background by learning the low-rank subspac...

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Bibliographic Details
Main Authors: Han, Guang, Zhang, Guanghao, Cai, Xi
Format: Conference Proceeding
Language:English
Subjects:
Adaptation models
adaptive weighting parameter
background initialization
low-rank subspace learning
Matrix decomposition
Measurement
online robust principal component analysis
Principal component analysis
Sparse matrices
Vehicle dynamics
Video sequences
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Summary:Background initialization is to estimate an appropriate representation for background of a scene, and has a decisive role in determining the performance of background subtraction. Background initialization based on low-rank subspace learning can obtain the background by learning the low-rank subspace. However, most of these methods are batch- based methods requiring heavy memory cost and unable to adapt to dynamic scenes. Accordingly, in this paper, we propose a background initialization method based on adaptive online low-rank subspace learning. The low-rank background subspace is estimated by online robust principal component analysis (PCA) in an online manner. An adaptive weighting parameter is utilized in the online robust PCA to enhance its ability to dynamically model the background. Experimental results demonstrate that, the proposed method can effectively gain the backgrounds of dynamic scenes.
ISSN:2164-5221
DOI:10.1109/ICSP48669.2020.9320960