Abstract
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.
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© 2006 Springer-Verlag Berlin Heidelberg
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Guan, W., Monger, P. (2006). Real-Time Detection of Out-of-Plane Objects in Stereo Vision. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2006. Lecture Notes in Computer Science, vol 4291. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11919476_11
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DOI: https://doi.org/10.1007/11919476_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-48628-2
Online ISBN: 978-3-540-48631-2
eBook Packages: Computer ScienceComputer Science (R0)