Real Time Face and Object Tracking as a Component of a Perceptual User Interface
WACV '98 Proceedings of the 4th IEEE Workshop on Applications of Computer Vision (WACV'98)
Using Histograms to Detect and Track Objects in Color Video
AIPR '01 Proceedings of the 30th on Applied Imagery Pattern Recognition Workshop
Integral Histogram: A Fast Way To Extract Histograms in Cartesian Spaces
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Orientation histogram-based matching for region tracking
WIAMIS '07 Proceedings of the Eight International Workshop on Image Analysis for Multimedia Interactive Services
Realtime Coarse Pose Recognition Using a Multi-scaled Local Integral Histograms
ICCIT '07 Proceedings of the 2007 International Conference on Convergence Information Technology
Image segmentation in video sequences: a probabilistic approach
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Robust detection of moving objects in video sequences through rough set theory framework
Image and Vision Computing
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Background Subtraction is an important preprocessing step for extracting the features of tracking objects in the vision-based HCI system. In this paper, the orientation histogram between the foreground image and the background image is compared to extract the foreground probability in the local area. The orientation histogram-based method is partially robust against illumination change and small moving objects in background. There are two major drawbacks of using histograms which are quantization errors in histogram binning and slow computation speed. With Gaussian binning and integral histogram, we present the recursive partitioning method that gives false detection suppression and fast computation speed.