A hierarchical approach to robust background subtraction using color and gradient information

By Omar Javed, Khurram Shafique, Mubarak Shah. Workshop on Motion and Video Computing, 2002. Proceedings., 2002

475 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/788853db3fe42fa192c332549b8df241b80fd642

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In Computer Vision and Pattern Recognition

Citations in the corpus

Consistent Labeling of Tracked Objects in Multiple Cameras with Overlapping Fields of View (2003, background): “Results of experiments with both indoor and outdoor sequences were presented.”
Multi-Layer Background Subtraction Based on Color and Texture (2007, background): “[5] proposed to integrate multiple cues (color and gradient information) to detect moving foreground objects in three distinct levels, i.”
Real-time foreground-background segmentation using codebook model (2005, unknown): “The subtraction identifies non-stationary or new objects.”
Background modeling and subtraction of dynamic scenes (2003, background): “Promising results demonstrate the potentials of the proposed framework.”
Recent Advanced Statistical Background Modeling for Foreground Detection - A Systematic Survey (2011, methodology): “Some authors used Markov Random Fields [107-109], hierarchical approaches [110-113], multi-level approaches [100, 114-118], multiple backgrounds [119, 121], graph cuts [81], multi-layer approaches [122, 123], tracking feedback [128, 129] or specific post-processing [130-131].”
Background Subtraction for Freely Moving Cameras (2009, background): “This has been achieved by the near ubiquitous assumption in modern surveillance systems of stationary (or nominally stationary) cameras, [11, 34, 4, 5, 27, 2, 12, 26].”
Bayesian modeling of dynamic scenes for object detection (2005, background): “a standard in background subtraction, as well as a basis for other approaches ([ 18 ],[15]).”
Background Modeling using Mixture of Gaussians for Foreground Detection - A Survey (2008, methodology): “[80] use the gradient as feature to deal with local sudden illumination changes (LS) and the ghosts leaved in (MBO) and (WFO).”
Motion-based background subtraction using adaptive kernel density estimation (2004, methodology): “The mixture-of-Gaussians method is quite popular and was to be the basis for a large number of related techniques [15] [13].”
Statistical modeling of complex backgrounds for foreground object detection (2004, methodology): “To be robust to illumination changes, some spatial features are also exploited [2], [10], [11].”