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
Non-parametric Model for Background Subtraction
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
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
Bayesian Modeling of Dynamic Scenes for Object Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Bayesian Approach to Background Modeling
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
A Texture-Based Method for Modeling the Background and Detecting Moving Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Foreground Detection In Video Using Pixel Layers
IEEE Transactions on Pattern Analysis and Machine Intelligence
Incremental Learning for Robust Visual Tracking
International Journal of Computer Vision
Robust foreground segmentation based on two effective background models
MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
Learning a scene background model via classification
IEEE Transactions on Signal Processing
Illumination invariant foreground detection using multi-subspace learning
International Journal of Knowledge-based and Intelligent Engineering Systems
Real-time foreground-background segmentation using codebook model
Real-Time Imaging
Statistical modeling of complex backgrounds for foreground object detection
IEEE Transactions on Image Processing
3D human modeling using virtual multi-view stereopsis and object-camera motion estimation
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
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We propose a new adaptive learning algorithm using multiple eigensubspaces to handle sudden as well as gradual changes in background due for example to illumination variations. To handle such changes, the feature space is organized into clusters representing the different background appearances. A local principal component analysis transformation is used to learn a separate eigensubspace for each cluster and an adaptive learning is used to continuously update the eigensubspaces. When the current image is presented, the system automatically selects a learned subspace that shares the closest appearance and lighting condition with the input image, which is then projected onto the subspace so that both background and foreground pixels can be classified. To efficiently adapt to changes in lighting conditions, an incremental update of the multiple eigensubspaces using synthetic background appearances is included in our framework. By doing so, our system can eliminate any noise or distortions that otherwise would incur from the existence of foreground objects, while it correctly updates the specific eigensubspace representing the current background appearance. A forgetting factor is also employed to control the contribution of earlier observations and limit the number of learned subspaces. As the extensive experimental results with various benchmark sequences demonstrate, the proposed algorithm outperforms, quantitatively and qualitatively, many other appearance-based approaches as well as methods using Gaussian Mixture Model (GMM), especially under sudden and drastic changes in illumination. Finally, the proposed algorithm is demonstrated to be linear with the size of the images d, the number of basis in the local PCA m, and the number of images used for adaptation n: that is, the algorithm is O(dmn) and our C++ implementation runs in real time - i.e. at frame rate for normal resolution (VGA) images.