Illumination invariant foreground detection using multi-subspace learning

Authors:
Y. Dong;T. X. Han;G. N. Desouza
Affiliations:
Electrical and Computer Engineering Department, University of Missouri, Columbia, MO, USA;Electrical and Computer Engineering Department, University of Missouri, Columbia, MO, USA;(Correspd. Tel.: +1 573 882 5579/ Fax: +1 573 882 0397/ E-mail: DeSouzaG@missouri.edu) Electrical and Computer Engineering Department, University of Missouri, Columbia, MO, USA
Venue:
International Journal of Knowledge-based and Intelligent Engineering Systems
Year:
2010

Citing 12
Cited 1

Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection

IEEE Transactions on Pattern Analysis and Machine Intelligence
Pfinder: Real-Time Tracking of the Human Body

IEEE Transactions on Pattern Analysis and Machine Intelligence
Dimension reduction by local principal component analysis

Neural Computation
A Bayesian Computer Vision System for Modeling Human Interactions

IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Learning of Finite Mixture Models

IEEE Transactions on Pattern Analysis and Machine Intelligence
Using Adaptive Tracking to Classify and Monitor Activities in a Site

CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Background Modeling and Subtraction of Dynamic Scenes

ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Effective Gaussian Mixture Learning for Video Background Subtraction

IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust and Efficient Foreground Analysis for Real-Time Video Surveillance

CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Evaluating Multi-Object Tracking

CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
Incremental Learning for Robust Visual Tracking

International Journal of Computer Vision
Statistical modeling of complex backgrounds for foreground object detection

IEEE Transactions on Image Processing

Adaptive learning of multi-subspace for foreground detection under illumination changes

Computer Vision and Image Understanding

Quantified Score

Hi-index	0.00

Visualization

Abstract

We propose a learning algorithm using multiple eigen subspaces to handle sudden illumination variations in background subtraction. The feature space is organized into clusters representing the different lighting conditions. A Local Principle Component Analysis (LPCA) transformation is used to learn a separate eigen subspace for each cluster. When a new image is presented, the system automatically selects a learned subspace that shares the closest lighting condition with the input image, which is then projected onto the subspace so the system can classify background and foreground pixels. The experimental results demonstrate that the proposed algorithm outperforms the original EigenBackground algorithm and Gaussian Mixture Model (GMM) especially under sudden illumination changes.