Pfinder: Real-Time Tracking of the Human Body
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Bayesian Computer Vision System for Modeling Human Interactions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Colour Model Selection and Adaption in Dynamic Scenes
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
Motion detection with nonstationary background
Machine Vision and Applications
W4: Who? When? Where? What? A Real Time System for Detecting and Tracking People
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Evaluation of global image thresholding for change detection
Pattern Recognition Letters
Background Modeling and Subtraction of Dynamic Scenes
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Probabilistic Space-Time Video Modeling via Piecewise GMM
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Dynamic Hidden Markov Random Field Model for Foreground and Shadow Segmentation
WACV-MOTION '05 Proceedings of the Seventh IEEE Workshops on Application of Computer Vision (WACV/MOTION'05) - Volume 1 - Volume 01
Background Subtraction Using Markov Thresholds
WACV-MOTION '05 Proceedings of the IEEE Workshop on Motion and Video Computing (WACV/MOTION'05) - Volume 2 - Volume 02
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 Dynamic Conditional Random Field Model for Foreground and Shadow Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Pixel-wise Object Tracking Algorithm with Target and Background Sample
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
Dynamic Control of Adaptive Mixture-of-Gaussians Background Model
AVSS '06 Proceedings of the IEEE International Conference on Video and Signal Based Surveillance
Flexible background mixture models for foreground segmentation
Image and Vision Computing
Moving object segmentation by background subtraction and temporal analysis
Image and Vision Computing
Smooth foreground-background segmentation for video processing
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part II
Real Time Foreground-Background Segmentation Using a Modified Codebook Model
AVSS '09 Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance
Adaptive model for object detection in noisy and fast-varying environment
ICIAP'11 Proceedings of the 16th international conference on Image analysis and processing: Part I
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Foreground segmentation is a fundamental first processing stage for vision systems which monitor real-world activity. In this paper, we consider the problem of achieving robust segmentation in scenes where the appearance of the background varies unpredictably over time. Variations may be caused by processes such as moving water, or foliage moved by wind, and typically degrade the performance of standard per-pixel background models. Our proposed approach addresses this problem by modeling homogeneous regions of scene pixels as an adaptive mixture of Gaussians in color and space. Model components are used to represent both the scene background and moving foreground objects. Newly observed pixel values are probabilistically classified, such that the spatial variance of the model components supports correct classification even when the background appearance is significantly distorted. We evaluate our method over several challenging video sequences, and compare our results with both per-pixel and Markov Random Field based models. Our results show the effectiveness of our approach in reducing incorrect classifications.