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@ARTICLE{Varadarajan_IJCV_2012,
         author = {Varadarajan, Jagannadan and Emonet, Remi and Odobez, Jean-Marc},
       keywords = {Unsupervised {\textperiodcentered} Latent sequential patterns {\textperiodcentered} Topic models {\textperiodcentered} PLSA {\textperiodcentered} Video surveillance {\textperiodcentered} Activity analysis},
       projects = {Idiap, HAI, VANAHEIM},
          month = may,
          title = {A Sequential Topic Model for Mining Recurrent Activities from Long Term Video Logs},
        journal = {International Journal of Computer Vision},
         volume = {103},
         number = {1},
           year = {2013},
          pages = {100-126},
       abstract = {This paper introduces a novel probabilistic activity modeling approach
that mines recurrent sequential patterns called motifs from documents given as
word×time count matrices (e.g., videos). In this model, documents are represented
as a mixture of sequential activity patterns (our motifs) where the mixing weights
are defined by the motif starting time occurrences. The novelties are multi fold.
First, unlike previous approaches where topics modeled only the co-occurrence of
words at a given time instant, our motifs model the co-occurrence and temporal
order in which the words occur within a temporal window. Second, unlike traditional
Dynamic Bayesian Networks (DBN), our model accounts for the important
case where activities occur concurrently in the video (but not necessarily in syn-
chrony), i.e., the advent of activity motifs can overlap. The learning of the motifs
in these difficult situations is made possible thanks to the introduction of latent
variables representing the activity starting times, enabling us to implicitly align the
occurrences of the same pattern during the joint inference of the motifs and their
starting times. As a third novelty, we propose a general method that favors the
recovery of sparse distributions, a highly desirable property in many topic model
applications, by adding simple regularization constraints on the searched distributions
to the data likelihood optimization criteria. We substantiate our claims with
experiments on synthetic data to demonstrate the algorithm behavior, and on four
video datasets with significant variations in their activity content obtained from
static cameras. We observe that using low-level motion features from videos, our
algorithm is able to capture sequential patterns that implicitly represent typical
trajectories of scene objects.},
            pdf = {https://publications.idiap.ch/attachments/papers/2012/Varadarajan_IJCV_2012.pdf}
}