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Conference Papers Year : 2010

Using multiple hypothesis in model-based tracking

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Abstract

Classic registration methods for model-based tracking try to align the projected edges of a 3D model with the edges of the image. However, wrong matches at low level can make these methods fail. This paper presents a new approach allowing to retrieve multiple hypothesis on the camera pose from multiple low-level hypothesis. These hypothesis are integrated into a particle filtering framework to guide the particle set toward the peaks of the distribution. Experiments on simulated and real video sequences show the improvement in robustness of the resulting tracker.

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Dates and versions

inria-00544795 , version 1 (09-12-2010)

Identifiers

  • HAL Id : inria-00544795 , version 1

Cite

Céline Teulière, E. Marchand, Laurent Eck. Using multiple hypothesis in model-based tracking. IEEE Int. Conf. on Robotics and Automation, ICRA'10, 2010, Anchorage, Alaska, United States. pp.4559-4565. ⟨inria-00544795⟩
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