Stationary background generation: an alternative to the difference of two images
Pattern Recognition
Smart Cameras as Embedded Systems
Computer
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
Background subtraction under varying illumination
Systems and Computers in Japan
The relationship between Precision-Recall and ROC curves
ICML '06 Proceedings of the 23rd international conference on Machine learning
Background Initialization in Cluttered Sequences
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
IEEE Transactions on Computers
Multi-Region Probabilistic Histograms for Robust and Scalable Identity Inference
ICB '09 Proceedings of the Third International Conference on Advances in Biometrics
Real-time foreground-background segmentation using codebook model
Real-Time Imaging
An efficient and robust sequential algorithm for background estimation in video surveillance
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Adaptive Patch-Based Background Modelling for Improved Foreground Object Segmentation and Tracking
AVSS '10 Proceedings of the 2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance
A novel robust statistical method for background initialization and visual surveillance
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part I
A Self-Organizing Approach to Background Subtraction for Visual Surveillance Applications
IEEE Transactions on Image Processing
Modeling Background and Segmenting Moving Objects from Compressed Video
IEEE Transactions on Circuits and Systems for Video Technology
A Robust Object Segmentation System Using a Probability-Based Background Extraction Algorithm
IEEE Transactions on Circuits and Systems for Video Technology
Shadow detection: A survey and comparative evaluation of recent methods
Pattern Recognition
An improved basic sequential clustering algorithm for background construction and motion detection
ICIAR'12 Proceedings of the 9th international conference on Image Analysis and Recognition - Volume Part I
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For the purposes of foreground estimation, the true background model is unavailable in many practical circumstances and needs to be estimated from cluttered image sequences. We propose a sequential technique for static background estimation in such conditions, with low computational and memory requirements. Image sequences are analysed on a block-by-block basis. For each block location a representative set is maintained which contains distinct blocks obtained along its temporal line. The background estimation is carried out in a Markov Random Field framework, where the optimal labelling solution is computed using iterated conditional modes. The clique potentials are computed based on the combined frequency response of the candidate block and its neighbourhood. It is assumed that the most appropriate block results in the smoothest response, indirectly enforcing the spatial continuity of structures within a scene. Experiments on real-life surveillance videos demonstrate that the proposedmethod obtains considerably better background estimates (both qualitatively and quantitatively) than median filtering and the recently proposed "intervals of stable intensity" method. Further experiments on the Wallflower dataset suggest that the combination of the proposed method with a foreground segmentation algorithm results in improved foreground segmentation.