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Conference papers

Motion Prediction for Moving Objects: a Statistical Approach

Dizan Alejandro Vasquez Govea 1 Thierry Fraichard 1
1 E-MOTION - Geometry and Probability for Motion and Action
GRAVIR - IMAG - Laboratoire d'informatique GRAphique, VIsion et Robotique de Grenoble, Inria Grenoble - Rhône-Alpes
Abstract : This paper proposes a technique to obtain long term estimates of the motion of a moving object in a structured environment. Objects moving in such environments often participate in typical motion patterns which can be observed consistently. Our technique learns those patterns by observing the environment and clustering the observed trajectories using any pairwise clustering algorithm. We have implemented our technique using both simulated and real data coming from a vision system. The results show that the technique is general, produces long-term predictions and is fast enough for its use in real time applications.
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Submitted on : Wednesday, October 24, 2007 - 6:45:37 PM
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  • HAL Id : inria-00182066, version 1




Dizan Alejandro Vasquez Govea, Thierry Fraichard. Motion Prediction for Moving Objects: a Statistical Approach. Proc. of the IEEE Int. Conf. on Robotics and Automation, Apr 2004, New Orleans, LA (US), France. pp.3931--3936. ⟨inria-00182066⟩



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