Combining Color, Depth, and Motion for Video Segmentation
ICVS '09 Proceedings of the 7th International Conference on Computer Vision Systems: Computer Vision Systems
An evaluation of pixel-based methods for the detection of floating objects on the sea surface
EURASIP Journal on Advances in Signal Processing - Special issue on advances in signal processing for maritime applications
Background subtraction for automated multisensor surveillance: a comprehensive review
EURASIP Journal on Advances in Signal Processing - Special issue on advanced image processing for defense and security applications
An edge-based approach for robust foreground detection
ACIVS'11 Proceedings of the 13th international conference on Advanced concepts for intelligent vision systems
A temporal-spatial background modeling of dynamic scenes
Frontiers of Computer Science in China
Background modeling by subspace learning on spatio-temporal patches
Pattern Recognition Letters
Block-Sparse RPCA for consistent foreground detection
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part V
Background subtraction with dirichlet processes
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part IV
Low-complexity scalable distributed multicamera tracking of humans
ACM Transactions on Sensor Networks (TOSN)
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Background subtraction is a crucial step in many automatic video content analysis applications. While numerous acceptable techniques have been proposed so far for background extraction, there is still a need to produce more efficient algorithms in terms of adaptability to multiple environments, noise resilience, and computation efficiency. In this paper, we present a powerful method for background extraction that improves in accuracy and reduces the computational load. The main innovation concerns the use of a random policy to select values to build a samples-based estimation of the background. To our knowledge, it is the first time that a random aggregation is used in the field of background extraction. In addition we propose a novel policy that propagates information between neighboring pixels of an image. Experiment detailed in this paper show how our method improves on other widely used techniques, and how it outperforms these techniques for noisy images.