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@ARTICLE{Emonet_IEEE-TRANSPAMI_2013,
         author = {Emonet, Remi and Varadarajan, Jagannadan and Odobez, Jean-Marc},
       keywords = {Bayesian modeling, camera network, mixed activity, motif mining, multi-camera, multivariate time series, non parametric models, topic models, unsupervised activity analysis},
       projects = {Idiap, HAI, VANAHEIM},
          month = jan,
          title = {Temporal Analysis of Motif Mixtures using Dirichlet Processes},
        journal = {IEEE Trans. Pattern Analysis and Machine Intelligence (PAMI)},
         volume = {36},
         number = {1},
           year = {2014},
       abstract = {n this article, we present a new model for unsupervised discovery of recurrent temporal patterns (or motifs) in time series (or documents). The model is designed to handle the difficult case of multivariate time series obtained from a mixture of activities, that is, our observations are caused by the superposition of multiple phenomena occurring concurrently and with no synchronization. The model uses non parametric Bayesian methods to describe both the motifs and their occurrences in documents. We derive an inference scheme to automatically and simultaneously recover the recurrent motifs (both their characteristics and number) and their occurrence instants in each document. The model is widely applicable and is illustrated on datasets coming from multiple modalities, mainly, videos from static cameras and audio localization data. The rich semantic interpretation that the model offers can be leveraged in tasks such as event counting or for scene analysis. The approach is also used as a mean of doing soft camera calibration in a camera network. A thorough study of the model parameters is provided and a cross-platform implementation of the inference algorithm will be made publicly available.},
            pdf = {https://publications.idiap.ch/attachments/papers/2013/Emonet_IEEE-TRANSPAMI_2013.pdf}
}