Comparison of the Efficiency of Deterministic and Stochastic Algorithms for Visual Reconstruction
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
A Hierarchical Approach to Robust Background Subtraction using Color and Gradient Information
MOTION '02 Proceedings of the Workshop on Motion and Video Computing
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
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
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
Efficient adaptive density estimation per image pixel for the task of background subtraction
Pattern Recognition Letters
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Reliable background suppression for complex scenes
Proceedings of the 4th ACM international workshop on Video surveillance and sensor networks
Robust Foreground Detection In Video Using Pixel Layers
IEEE Transactions on Pattern Analysis and Machine Intelligence
Markov Random Field Modeling in Image Analysis
Markov Random Field Modeling in Image Analysis
Object-Wise Multilayer Background Ordering for Public Area Surveillance
AVSS '09 Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance
Learning a scene background model via classification
IEEE Transactions on Signal Processing
Real-time foreground-background segmentation using codebook model
Real-Time Imaging
Segmenting Video Foreground Using a Multi-Class MRF
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Color Adjacency Modeling for Improved Image and Video Segmentation
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
PRISMATICA: toward ambient intelligence in public transport environments
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Detecting moving objects, ghosts, and shadows in video streams
IEEE Transactions on Pattern Analysis and Machine Intelligence
Region-Level Motion-Based Background Modeling and Subtraction Using MRFs
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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Foreground detection methods generally assume that backgrounds are observed more frequently than foregrounds are, but the assumption is not valid in public scenes. Viewing background adaptation in public scenes as a unified problem with background initialization and stationary object detection, we formulate it as an energy minimization problem in dynamic Markov random fields. Constraining the connections among the sites with spatiotemporal reliabilities, we robustly handle object-wise changes and efficiently minimize the energy terms with a coordinate descent method. Evaluated with realistic sequences from i-LIDS, PETS, ETISEO and changedetection.net datasets, the proposed method outperforms state-of-the-art methods and temporal parameter adjustment.