Pfinder: Real-Time Tracking of the Human Body
IEEE Transactions on Pattern Analysis and Machine Intelligence
An introduction to variational methods for graphical models
Learning in graphical models
W4: Real-Time Surveillance of People and Their Activities
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
Markov random field modeling in image analysis
Markov random field modeling in image analysis
A Probabilistic Background Model for Tracking
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
Loopy belief propagation for approximate inference: an empirical study
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Markovian framework for foreground-background-shadow separation of real world video scenes
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part I
Iterative division and correlograms for detection and tracking of moving objects
IWICPAS'06 Proceedings of the 2006 Advances in Machine Vision, Image Processing, and Pattern Analysis international conference on Intelligent Computing in Pattern Analysis/Synthesis
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Robust and accurate background segmentation is crucial for surveillance applications and is a key element in visual tracking, layer-based compression, and silhouette-based 3D reconstruction. In this paper, we present a novel spatial-temporal model that describes the appearance and dynamics of background scenes at multiple resolutions. We propose a time-dependent Markov Random Field (MRF) to represent the state of foreground and background at each pixel in the spatial-temporal pyramid. Pixels are linked spatially and temporally across frames. The probability of adding/deleting a foreground object is calculated by online learning algorithm and is used as prior information in computing foreground label. We use Gibbs Sampling to solve the MRF in a Maximum A Posterior (MAP) framework. Experimental results show that this real-time algorithm is able to segment the foreground object accurately from videos and more resilient to distractions such as imaging noise, illumination changes, camera shakes, and random motion in the scene.