%Aigaion2 BibTeX export from Idiap Publications
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@INPROCEEDINGS{ba-icpr-2004,
         author = {Ba, Sil{\`{e}}ye O. and Odobez, Jean-Marc},
       projects = {Idiap},
          month = {8},
          title = {A probabilistic framework for joint head tracking and pose estimation},
      booktitle = {17th Int. Conf. Pattern Recognition (ICPR)},
         volume = {4},
           year = {2004},
        address = {Cambridge, UK},
           note = {Similar to RR-03-78.},
       crossref = {odobez-rr-03-78},
       abstract = {Head Tracking and pose estimation are usually considered as two sequential and separate problems: pose is estimated on the head patch provided by a tracking module. However, precision in head pose estimation is dependent on tracking accuracy which itself could benefit from the head orientation knowledge. Therefore, this work considers head tracking and pose estimation as two coupled problems in a probabilistic setting. Head pose models are learned and incorporated into a mixed-state particle filter framework for joint head tracking and pose estimation. Experimental results on real sequences show the effectiveness of the method in estimating more stable and accurate pose values.},
            pdf = {https://publications.idiap.ch/attachments/reports/2004/odobez_2004_icpr2.pdf},
     postscript = {ftp://ftp.idiap.ch/pub/reports/2004/odobez_2004_icpr2.ps.gz},
ipdinar={2004},
ipdmembership={vision},
ipdpriority={2},
}



crossreferenced publications: 
@TECHREPORT{odobez-rr-03-78,
         author = {Ba, Sil{\`{e}}ye O. and Odobez, Jean-Marc},
       projects = {Idiap},
          title = {A {P}robabilistic {F}ramework for {J}oint {H}ead {T}racking and {P}ose Estimation},
           type = {Idiap-RR},
         number = {Idiap-RR-78-2003},
           year = {2003},
    institution = {IDIAP},
        address = {Martigny, Switzerland},
           note = {Published in International Conference on Pattern Recognition (ICPR,',','),
 2004},
       abstract = {Head Tracking and pose estimation are usually considered as two sequential and separate problems: pose is estimated on the head patch provided by a tracking module. However, precision in head pose estimation is dependent on tracking accuracy which itself could benefit from the head orientation knowledge. Therefore, this work considers head tracking and pose estimation as two coupled problems in a probabilistic setting. Head pose models are learned and incorporated into a mixed-state particle filter framework for joint head tracking and pose estimation. Experimental results on real sequences show the effectiveness of the method in estimating more stable and accurate pose values.},
            pdf = {https://publications.idiap.ch/attachments/reports/2003/rr03-78.pdf},
     postscript = {ftp://ftp.idiap.ch/pub/reports/2003/rr03-78.ps.gz},
ipdinar={2003},
ipdmembership={vision},
language={English},
}