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@TECHREPORT{smith:rr06-40,
         author = {Smith, Kevin C. and Ba, Sil{\`{e}}ye O. and Odobez, Jean-Marc and Gatica-Perez, Daniel},
       projects = {Idiap},
          title = {Tracking Attention for Multiple People: Wandering Visual Focus of Attention Estimation},
           type = {Idiap-RR},
         number = {Idiap-RR-40-2006},
           year = {2006},
    institution = {IDIAP},
           note = {Submitted for publication},
       abstract = {The problem of finding the visual focus of attention of multiple people free to move in an unconstrained manner is defined here as the {\em wandering visual focus of attention} (WVFOA) problem. Estimating the WVFOA for multiple unconstrained people is a new and important problem with implications for human behavior understanding and cognitive science, as well as real-world applications. One such application, which we present in this article, monitors the attention passers-by pay to an outdoor advertisement. In our approach to the WVFOA problem, we propose a multi-person tracking solution based on a hybrid Dynamic Bayesian Network that simultaneously infers the number of people in a scene, their body locations, their head locations, and their head pose. It is defined in a joint state-space formulation that allows for the modeling of interactions between people. For inference in the resulting high-dimensional state-space, we propose a trans-dimensional Markov Chain Monte Carlo (MCMC) sampling scheme, which not only handles a varying number of people, but also efficiently searches the state-space by allowing person-part state updates. Our model was rigorously evaluated for tracking quality and ability to recognize people looking at an outdoor advertisement, and the results indicate good performance for these tasks.},
            pdf = {https://publications.idiap.ch/attachments/reports/2006/smith-idiap-rr-06-40.pdf},
     postscript = {ftp://ftp.idiap.ch/pub/reports/2006/smith-idiap-rr-06-40.ps.gz},
ipdmembership={vision},
}