1 real time illumination invariant background subtraction using local kernel histograms.

Content Provider	CiteSeerX
Author	Bernier, Olivier
Abstract	Constant background hypothesis for background subtraction algorithms is often not applicable in real environments because of shadows, reflections, or small moving objects in the background: flickering screens in indoor scenes, or waving vegetation in outdoor ones. In both indoor and outdoor scenes, the use of color cues for background segmentation is limited by illumination variations when lights are switched or weather changes. This problem can be partially allievated using robust color coordinates or background update algorithms but an important part of the color information is lost by the former solution and the latter is often too specialized to cope with most of real environment constraints. This paper presents an approach using local kernel histograms and contour-based features. Local kernel histograms have the conventional histograms advantages avoiding their inherent drawbacks. Contour based features are more robust than color features regarding scene illumination variations. The proposed algorithm performances are emphasized in the experimental results using test scenes involving strong illumination variations and non static backgrounds. 1
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Subject Keyword	Local Kernel Histogram Color Feature Illumination Variation Outdoor Scene Robust Color Coordinate Non Static Background Background Subtraction Algorithm Real Environment Scene Illumination Variation Test Scene Indoor Scene Contour-based Feature Real Environment Constraint Algorithm Performance Strong Illumination Variation Background Segmentation Inherent Drawback Flickering Screen Outdoor One Color Information Background Update Algorithm Constant Background Hypothesis Color Cue Former Solution Conventional Histogram Advantage Experimental Result Important Part
Content Type	Text
Resource Type	Article