Pfinder: Real-Time Tracking of the Human Body
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Linear Programming Boosting via Column Generation
Machine Learning
A Fast Background Scene Modeling and Maintenance for Outdoor Surveillance
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 4
Sparse bayesian learning and the relevance vector machine
The Journal of Machine Learning Research
Statistical Background Subtraction for a Mobile Observer
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
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
Background Estimation as a Labeling Problem
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
A Texture-Based Method for Modeling the Background and Detecting Moving Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Learning appearance and transparency manifolds of occluded objects in layers
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Image segmentation in video sequences: a probabilistic approach
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Detecting moving objects, ghosts, and shadows in video streams
IEEE Transactions on Pattern Analysis and Machine Intelligence
Figure-ground segmentation from occlusion
IEEE Transactions on Image Processing
Adaptive learning of multi-subspace for foreground detection under illumination changes
Computer Vision and Image Understanding
Background modeling by subspace learning on spatio-temporal patches
Pattern Recognition Letters
A unified approach to background adaptation and initialization in public scenes
Pattern Recognition
| Hi-index | 35.68 |
Learning to efficiently construct a scene background model is crucial for tracking techniques relying on background subtraction. Our proposed method is motivated by criteria leading to what a general and reasonable background model should be, and realized by a practical classification technique. Specifically, we consider a two-level approximation scheme that elegantly combines the bottom-up and top-down information for deriving a background model in real time. The key idea of our approach is simple but effective: If a classifier can be used to determme which Image blocks are part of the background, its outcomes can help to carry out appropriate blockwise updates in learning such a model. The quality of the solution is further improved by global validations of the local updates to maintain the interblock consistency. And a complete background model can then be obtained based on a measurement of model completion. To demonstrate the effectiveness of our method, various experimental results and comparisons are included.