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Appearance Management
and Cue Fusion for 3D Model-Based Tracking
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| Jump to: Complete Reference, Abstract, Related Download, BibTeX Entry, Links and Notes, Images, Additional Material | |
| Complete Reference | |
| N. Krahnstoever, R. Sharma, "Appearance Management and Cue Fusion for 3D Model-Based Tracking," IEEE Conference on Computer Vision and Pattern Recognition, Madison, Wisconsin, June 16-22, 2003, accepted for publication. | |
| Abstract | |
| This paper presents a systematic approach to
acquiring model appearance information online and to use the acquired information
to drive a set of complementary imaging cues to obtain a discriminatory,
drift-free, observation model. Appearance is modeled as a Markov Random
Field of color distributions over the model surface. The online appearance
acquisition process estimates appearance based on uncertain image measurements
and is designed to greatly reduce the chance of mapping non-object image
data onto the model. Confidences about the different appearance driven imaging
cues are estimated in order adaptively balance the contributions of the
different cues which allows to maintain performance in the presence of degradation
in imaging conditions. The discriminatory power of the resulting model is
good enough to allow long-duration single-hypothesis model based tracking
under flexible imaging conditions with no prior appearance information. The performance of the resulting generative model-based tracker is evaluated carefully based on real and semi-synthetic video sequences, showing that the presented algorithm is able to robustly track a vide variety of targets under challenging imaging conditions. |
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| Related Download | |
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[ PDF (3.3MB) ] |
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| BibTeX Entry | |
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booktitle={Proc. of IEEE Conference on Computer Vision and Pattern Recognition, Madison, Wisconsin, USA}, month={June}, title={Appearance Management and Cue Fusion for {3D} Model-Based Tracking}, year={2003} |
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| Links and Notes | |
| CVPR 2003 Conference | |
| Images | |
| Additional Material | |
| There is also an updated and more detailed Technical Report: [ Hires PDF (11MB) | Lores PDF (6.6MB) ] | |