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4-D Tensor Voting Motion Segmentation for Obstacle Detection in Autonomous Guided Vehicle

Abstract : Creating an obstacle detection system is an important challenge to improve safety for road vehicles. A way to meet the industrial cost requirements is to gather a monocular vision sensor. This paper tackles this problem and defines an highly parallelisable image motion segmentation method for taking into account the current evolution of multi processor computer technology. A complete and modular solution is proposed, based on the Tensor Voting framework extended to the 4D space (x, y, dx, dy), where surfaces describe homogeneous moving areas in the image plan.Watershed segmentation is applied on the result to obtain closed boundaries. Cells are then clustered and labeled with respect to planar parallax rigidity constraints. A visual odometry method, based on texture learning and tracking, is used to estimate residual parallax displacement.
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Submitted on : Wednesday, July 2, 2008 - 2:26:12 PM
Last modification on : Thursday, February 11, 2021 - 2:58:06 PM
Long-term archiving on: : Friday, May 28, 2010 - 7:47:39 PM


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Yann Dumortier, Isabelle Herlin, André Ducrot. 4-D Tensor Voting Motion Segmentation for Obstacle Detection in Autonomous Guided Vehicle. Intelligent Vehicles Symposium, IEEE, Jun 2008, Eindhoven, Netherlands. pp.379-384, ⟨10.1109/IVS.2008.4621203⟩. ⟨inria-00292702⟩



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