Background Modeling Approach Based on Eigenspace Decomposition

Abstract:

In this work principal component analysis (PCA) was adopted to construct a background model and moving objects were detected by background subtraction method. Firstly, constructed the matrix of training samples by means of converting the video sequence to vectors. Then calculated the covariance matrix C of the training set, and acquired the eigenvalues and eigenvectors of C through SVD decomposition. Next, sorted the eigenvalues and reconstructed the background model by using several image vectors which had higher cumulative contribution. Finally, comparison experiments are performed with the detection results by GMM approach. Experimental results show that the proposed method in this paper could establish background models more accurate and have better effective of object detection.

• Periodical: Applied Mechanics and Materials (Volumes 157-158)
• Pages: 1399-1403
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.157-158.1399
• Online since: February 2012
• Authors: Jian Wu Long, Xuan Jing Shen, Hai Peng Chen
• Keywords: Background Modeling, Eigenspace, Objects Detecting, Principal Component Analysis (PCA), SVD Decomposition

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