Model-based object tracking using stereo vision

Robust and accurate object recognition and tracking is a necessary pre-requisite for visual servoing tasks. This paper presents a new approach using disparity images provided by a stereo vision system from which 3D models are reconstructed. We use a segmentation procedure based on a simple region gr...

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Bibliographic Details
Main Authors: Ginhoux, R., Gutmann, J.-S.
Format: Conference Proceeding
Language:English
Subjects:
Clustering algorithms
Histograms
Image reconstruction
Image segmentation
Image sensors
Image sequences
Noise robustness
Object recognition
Stereo vision
Visual servoing
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Summary:Robust and accurate object recognition and tracking is a necessary pre-requisite for visual servoing tasks. This paper presents a new approach using disparity images provided by a stereo vision system from which 3D models are reconstructed. We use a segmentation procedure based on a simple region growing process to segment the non-dense 3D images, and compare it to a classical dense range image segmenter. The obtained regions are classified according to a simple model of the target object, and multiple candidates are handled over image sequences by application of the CONDENSATION algorithm. We then use a histogram based clusterization technique to identify the target object pose for visual servoing commands. Experimental results in localizing a door-handle show an accuracy of about 10 cm for the estimated position, while being robust to clutter and noise in the sensor images.
ISSN:1050-4729
2577-087X
DOI:10.1109/ROBOT.2001.932778