Real-Time Subspace-Based Background Modeling Using Multi-channel Data

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.