Pfinder: Real-Time Tracking of the Human Body
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Bayesian Computer Vision System for Modeling Human Interactions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Merging and Splitting Eigenspace Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Framework for Robust Subspace Learning
International Journal of Computer Vision - Special Issue on Computational Vision at Brown University
Candid Covariance-Free Incremental Principal Component Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Effective Gaussian Mixture Learning for Video Background Subtraction
IEEE Transactions on Pattern Analysis and Machine Intelligence
The equivalence of two-dimensional PCA to line-based PCA
Pattern Recognition Letters
Neural Networks - 2005 Special issue: IJCNN 2005
Complete Two-Dimensional PCA for Face Recognition
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Image segmentation in video sequences: a probabilistic approach
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Sequential Karhunen-Loeve basis extraction and its application to images
IEEE Transactions on Image Processing
Real-time robust background subtraction under rapidly changing illumination conditions
Image and Vision Computing
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Background modeling and subtraction using subspaces is attractive in real-time computer vision applications due to its low computational cost. However, the application of this method is mostly limited to the gray-scale images since the integration of multi-channel data is not straightforward; it involves much higher dimensional space and causes additional difficulty to manage data in general. We propose an efficient background modeling and subtraction algorithm using 2-Dimensional Principal Component Analysis (2DPCA) [1], where multi-channel data are naturally integrated in eigenbackground framework [2] with no additional dimensionality. It is shown that the principal components in 2DPCA are computed efficiently by transformation to standard PCA.We also propose an incremental algorithm to update eigenvectors to handle temporal variations of background. The proposed algorithm is applied to 3-channel (RGB) and 4-channel (RGB+IR) data, and compared with standard subspace-based as well as pixel-wise density-based method.