Real-Time Detection of Out-of-Plane Objects in Stereo Vision
This paper proposes an automatic approach to detecting objects appearing in front of planar background. A planar homography is estimated with high accuracy in an off-line initialization phase. Given a pair of binocular images, we apply the estimated homography to one of the images, and then compute a similarity map between the transformed image and the other. Normalized cross-correlation is used in the computation of the similarity map to measure the similarity between neighborhoods of overlapping pixels. Normalized cross-correlation measure is superior to absolute difference in alleviating the influence of image noise and small mis-alignment caused by imperfect homography estimation. The similarity map with pixel intensities ranging between 0 and 1 leads to an easy detection of out-of-plane objects because the values of pixels corresponding to planar background are close to 1. Tracking could be incorporated with our out-of-plane object detection method to further improve robustness in live video applications. This approach has been used in tracking people and demonstrated reliable performance.
KeywordsStereo Vision Integral Image Foreground Object Checkerboard Pattern Direct Linear Transformation
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