Using multiple hypothesis in model-based tracking

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 multi...

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Bibliographic Details
Published in2010 IEEE International Conference on Robotics and Automation pp. 4559 - 4565
Main Authors Teulière, Céline, Marchand, Eric, Eck, Laurent
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.05.2010
Subjects
Cameras
Filtering
Image edge detection
Particle tracking
Predictive models
Robot vision systems
Robotics and automation
Robustness
USA Councils
Video sequences
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Summary: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.
ISBN:9781424450381
1424450381
ISSN:1050-4729
DOI:10.1109/ROBOT.2010.5509284