Simultaneous Object Pose and Velocity Computation Using a Single View from a Rolling Shutter Camera
An original concept for computing instantaneous 3D pose and 3D velocity of fast moving objects using a single view is proposed, implemented and validated. It takes advantage of the image deformations induced by rolling shutter in CMOS image sensors. First of all, after analysing the rolling shutter phenomenon, we introduce an original model of the image formation when using such a camera, based on a general model of moving rigid sets of 3D points. Using 2D-3D point correspondences, we derive two complementary methods, compensating for the rolling shutter deformations to deliver an accurate 3D pose and exploiting them to also estimate the full 3D velocity. The first solution is a general one based on non-linear optimization and bundle adjustment, usable for any object, while the second one is a closed-form linear solution valid for planar objects. The resulting algorithms enable us to transform a CMOS low cost and low power camera into an innovative and powerful velocity sensor. Finally, experimental results with real data confirm the relevance and accuracy of the approach.
KeywordsClassical Algorithm Single View Bundle Adjustment Velocity Parameter Velocity Computation
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- 1.Ait-Aider, O., Andreff, N., Lavest, J.M., Martinet, P.: Exploiting rolling shutter distortions for simultaneous object pose and velocity computation using a single view. In: Proc. IEEE International Conference on Computer Vision Systems, New York, USA (January 2006)Google Scholar
- 5.Lavest, J.-M., Viala, M., Dhome, M.: Do we really need an accurate calibration pattern to achieve a reliable camera calibration? In: Burkhardt, H.-J., Neumann, B. (eds.) ECCV 1998. LNCS, vol. 1406, pp. 158–174. Springer, Heidelberg (1998)Google Scholar
- 7.Meingast, M., Geyer, C., Sastry, S.: Geometric models of rolling-shutter cameras. In: Proc. of the 6th Workshop on Omnidirectional Vision, Camera Networks and Non-Classical Cameras, Beijing, China (October 2005)Google Scholar
- 8.Phong, T.Q., Horaud, R., Tao, P.D.: Object pose from 2-d to 3-d point and line correspondences. International Journal of Computer Vision, 225–243 (1995)Google Scholar
- 9.Theuwissen, A.J.P.: Solid-state imaging with chargecoupled devices. Kluwer Academic Publishers, Dordrecht (1995)Google Scholar
- 10.Tsai, R.Y.: An efficient and accurate camera calibration technique for 3d machine vision. In: Proc. IEEE Conference on Computer Vision and Pattern Recognition, Miami Beach, pp. 364–374 (1986)Google Scholar
- 11.Wilburn, B., Joshi, N., Vaish, V., Levoy, M., Horowitz, M.: High-speed videography using a dense camera array. In: IEEE Society Conference on Pattern Recognition, CVPR 2004 (2004)Google Scholar