%Aigaion2 BibTeX export from Idiap Publications
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@INPROCEEDINGS{Emonet_CVPR_2011,
         author = {Emonet, Remi and Varadarajan, Jagannadan and Odobez, Jean-Marc},
       projects = {Idiap, SNSF-MULTI, VANAHEIM},
          month = jun,
          title = {Extracting and Locating Temporal Motifs in Video Scenes Using a Hierarchical Non Parametric Bayesian Model},
      booktitle = {IEEE Conference on Computer Vision and Pattern Recognition},
           year = {2011},
       abstract = {In this paper, we present an unsupervised method for
mining activities in videos. From unlabeled video sequences
of a scene, our method can automatically recover what
are the recurrent temporal activity patterns (or motifs) and
when they occur. Using non parametric Bayesian methods,
we are able to automatically find both the underlying number
of motifs and the number of motif occurrences in each
document. The model’s robustness is first validated on
synthetic data. It is then applied on a large set of video data
from state-of-the-art papers. We show that it can effectively
recover temporal activities with high semantics for humans
and strong temporal information. The model is also used
for prediction where it is shown to be as efficient as other
approaches. Although illustrated on video sequences, this
model can be directly applied to various kinds of time series
where multiple activities occur simultaneously.},
            pdf = {https://publications.idiap.ch/attachments/papers/2011/Emonet_CVPR_2011.pdf}
}