Pfinder: Real-Time Tracking of the Human Body
IEEE Transactions on Pattern Analysis and Machine Intelligence
Non-parametric Model for Background Subtraction
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
A Bayesian Computer Vision System for Modeling Human Interaction
ICVS '99 Proceedings of the First International Conference on Computer Vision Systems
Weighted and Robust Incremental Method for Subspace Learning
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
IMMC: incremental maximum margin criterion
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Robust foreground segmentation based on two effective background models
MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
Incremental subspace learning via non-negative matrix factorization
Pattern Recognition
Robust Foreground Segmentation Using Subspace Based Background Model
APCIP '09 Proceedings of the 2009 Asia-Pacific Conference on Information Processing - Volume 02
Independent component analysis-based background subtraction for indoor surveillance
IEEE Transactions on Image Processing
Detection of moving objects by independent component analysis
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part II
An incremental linear discriminant analysis using fixed point method
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
Overview and recent advances in partial least squares
SLSFS'05 Proceedings of the 2005 international conference on Subspace, Latent Structure and Feature Selection
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Subspace learning methods are widely used in background modeling to tackle illumination changes. Their main advantage is that it doesn't need to label data during the training and running phase. Recently, White et al. [1] have shown that a supervised approach can improved significantly the robustness in background modeling. Following this idea, we propose to model the background via a supervised subspace learning called Incremental Maximum Margin Criterion (IMMC). The proposed scheme enables to initialize robustly the background and to update incrementally the eigenvectors and eigenvalues. Experimental results made on the Wallflower datasets show the pertinence of the proposed approach.