CSDL Home IEEE Transactions on Pattern Analysis & Machine Intelligence 2014 vol.36 Issue No.01 - Jan.
Issue No.01 - Jan. (2014 vol.36)
Remi Emonet , Idiap Res. Inst., Martigny, Switzerland
Jagannadan Varadarajan , Idiap Res. Inst., Martigny, Switzerland
Jean-Marc Odobez , Idiap Res. Inst., Martigny, Switzerland
In this paper, 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 nonparametric 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.
multivariate time series, Motif mining, mixed activity, unsupervised activity analysis, topic models, multicamera, camera network, nonparametric models, Bayesian modeling,
Remi Emonet, Jagannadan Varadarajan, Jean-Marc Odobez, "Temporal Analysis of Motif Mixtures Using Dirichlet Processes", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.36, no. 1, pp. 140-156, Jan. 2014, doi:10.1109/TPAMI.2013.100