A survey of advances in vision-based human motion capture and analysis
Computer Vision and Image Understanding - Special issue on modeling people: Vision-based understanding of a person's shape, appearance, movement, and behaviour
Background estimation under rapid gain change in thermal imagery
Computer Vision and Image Understanding
International Journal of Web and Grid Services
Moving object segmentation by background subtraction and temporal analysis
Image and Vision Computing
Local manipulation of image layers using standard image processing primitives
Proceedings of the Seventh Indian Conference on Computer Vision, Graphics and Image Processing
N-view human silhouette segmentation in cluttered, partially changing environments
Proceedings of the 32nd DAGM conference on Pattern recognition
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
Vision-based user-centric light control for smart environments
Pervasive and Mobile Computing
Spatio-temporal optimization for foreground/background segmentation
ACCV'10 Proceedings of the 2010 international conference on Computer vision - Volume Part I
Background modeling by subspace learning on spatio-temporal patches
Pattern Recognition Letters
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Background modeling and subtraction to detect new or moving objects in a scene is an important component of many intelligent video applications. Compared to a single camera, the use of multiple cameras leads to better handling of shadows, specularities and illumination changes due to the utilization of geometric information. Although the result of stereo matching can be used as the feature for detection, it has been shown that the detection process can be made much faster by a simple subtraction of the intensities observed at stereo-generated conjugate pairs in the two views. The method, however, suffers from false and missed detections due to some geometric considerations. In this paper, we perform a detailed analysis of such errors. Then, we propose a sensor configuration that eliminates false detections. Algorithms are also proposed that effectively eliminate most detection errors due to missed detections, specular reflections and objects being geometrically close to the background. Experiments on several scenes illustrate the utility and enhanced performance of the proposed approach compared to existing techniques.