Automatic human detecting and tracking using stereo vision technique

A fully automatic system for human detection and tracking in front of an Interactive Whiteboard is presented. When a person is between a projector and the projection area, deleterious effects can be created from light shining on the face. We developed a stereo vision system that can be used to mitigate problems arising from this issue by accurately detecting the human body and masking the face. We present two main parts of this system: namely, automatic system calibration and the human detection and tracking. We use a checkerboard pattern that is projected on the whiteboard at start-up for automatic calibration. Grid patterns from two images are processed, and points between them are detected and localized. A projective transform is used to set the homography between the two images. Testing shows precise automatic calibration, with an average RMS error of 0.4 pixels in the off-line test. Human detection and tracking is accomplished using a similarity measure, foreground segmentation, principle component analysis, body shape feature extraction, disparity measure, and location estimation. We achieved an average detection rate of 97.7 % in the off-line tests. The method was fully implemented in a real-time system and testing showed the system to be very robust.