Illumination compensation based change detection using order consistency

We present a change detection method resistant to global and local illumination variations for use in visual surveillance scenarios. Approaches designed thus far for robustness to illumination change are generally based either on color normalization, texture (e.g. edges, rank order statistics, etc.), or illumination compensation. Normalization based methods sacrifice discriminability while texture based methods cannot operate on texture-less regions. Both types of method can produce large missing regions in the distance image which in turn pose problems for higherlevel processing tasks that may be shape or region-based and require accurate foreground masks (e.g. person detection and tracking, crowd segmentation, etc.). Texture based methods have an additional problem in that they produce false alarms due to textures induced by local illumination effects (e.g. cast shadows). In this paper we propose a compensation based approach for change detection. Prior work on compensation has lar...

• Type: Conference
• Year: 2010
• Where: CVPR
• Authors: Vasu Parameswaran, Maneesh Singh, Visvanathan Ramesh
• Keywords: Change Detection, Computer Vision, CVPR 2010, Local Illumination, Texture Based Methods