Boxlets: a fast convolution algorithm for signal processing and neural networks
Proceedings of the 1998 conference on Advances in neural information processing systems II
A Framework for High-Level Feedback to Adaptive, Per-Pixel, Mixture-of-Gaussian Background Models
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
Non-parametric Model for Background Subtraction
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
Summed-area tables for texture mapping
SIGGRAPH '84 Proceedings of the 11th annual conference on Computer graphics and interactive techniques
Silhouette Analysis-Based Gait Recognition for Human Identification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Improved Adaptive Gaussian Mixture Model for Background Subtraction
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
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
Bilayer Segmentation of Live Video
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
ACM Computing Surveys (CSUR)
Efficient hierarchical method for background subtraction
Pattern Recognition
Learning and Removing Cast Shadows through a Multidistribution Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scene segmentation based on IPCA for visual surveillance
Neurocomputing
Image segmentation in video sequences: a probabilistic approach
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Evaluation of background subtraction techniques for video surveillance
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Detecting moving objects, ghosts, and shadows in video streams
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical modeling of complex backgrounds for foreground object detection
IEEE Transactions on Image Processing
A Self-Organizing Approach to Background Subtraction for Visual Surveillance Applications
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
ViBe: A Universal Background Subtraction Algorithm for Video Sequences
IEEE Transactions on Image Processing
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Mixture of Gaussians (MoG) is well-known for effectively in sustaining background variations, which has been widely adopted for background subtraction. However, in complex backgrounds, MoG often traps in keeping balance between model convergence speed and its stability. The main difficulty is the selection of learning rates. In this paper, an effective learning strategy is proposed to provide better regularization of background adaptation for MoG. First, the video-data is splitted into the future-data and history-data, then a set of background distributions (MoG) is computed for each case. To distinguish between dynamic and static background, the equality of these two sets is tested by the hypothesis testing method. Next, a two-layer LBP-based method is proposed for foreground classification. Finally, the global and static learning rates are replaced by the adaptive learning rates for image pixels with distinct properties for each frame. By means of the proposed learning strategy, a novel background modeling for detecting foreground objects from complex environments is established. We compare our procedure against the state-of-the-art alternatives, the experimental results show that the performance of learning speed and accuracy obtained by proposed learning rate control strategy is better than existing MoG approaches.