%Aigaion2 BibTeX export from Idiap Publications
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@INPROCEEDINGS{Naturel_ICPR_2008,
         author = {Naturel, Xavier and Odobez, Jean-Marc},
       projects = {Idiap, CARETAKER},
          month = {12},
          title = {Detecting queues at vending machines: a statistical layered approach},
      booktitle = {Proc. Int. Conf. on Pattern Recognition (ICPR)},
           year = {2008},
       location = {Tampa},
       crossref = {naturel:rr08-04},
       abstract = {This paper presents a method for monitoring activities at a ticket vending machine in a video-surveillance context. Rather than relying on the output of a tracking module, which is prone to errors, the events are direclty recognized from image measurements. This especially does not require tracking. A statistical layered approach is proposed, where in the first layer, several sub-events are defined and detected using a discriminative approach. The second layer uses the result of the first and models the temporal relationships of the high-level event using a Hidden Markov Model (HMM). Results are assessed on 3h30 hours of real video footage coming from Turin metro station.},
            pdf = {https://publications.idiap.ch/attachments/papers/2008/Naturel_ICPR_2008.pdf}
}



crossreferenced publications: 
@TECHREPORT{naturel:rr08-04,
         author = {Naturel, Xavier and Odobez, Jean-Marc},
       projects = {Idiap},
          title = {Detecting queues at vending machines: a statistical layered approach},
           type = {Idiap-RR},
         number = {Idiap-RR-04-2008},
           year = {2008},
    institution = {IDIAP},
       abstract = {In this report, a method for monitoring activity at a ticket machine is presented. While this work has been done in the specific context of Turin metro, the proposed model could be applied to other locations and tasks in video-surveillance. Monitoring the activity is based here on event recognition, by modelling directly the events of interest.We especially focus on detecting queues at ticket vending machines. A 2-layer model is proposed. In the first layer, several sub-events are defined and detected using a discriminative approach (SVMs). The second layer uses the result of the first and model the high-level event of interest. Results are assessed on 4 hours of real video footage coming from Turin metro station.},
            pdf = {https://publications.idiap.ch/attachments/reports/2008/naturel-idiap-rr-08-04.pdf},
     postscript = {ftp://ftp.idiap.ch/pub/reports/2008/naturel-idiap-rr-08-04.ps.gz},
ipdmembership={vision},
}