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Multiperson Tracking by Online Learned Grouping Model With Nonlinear Motion Context
Bayesian Inference of Recursive Sequences of Group Activities from Tracks
We present a probabilistic generative model for inferring a description of
coordinated, recursively structured group activities at multiple levels of
temporal granularity based on observations of individuals' trajectories. The
model accommodates: (1) hierarchically structured groups, (2) activities that
are temporally and compositionally recursive, (3) component roles assigning
different subactivity dynamics to subgroups of participants, and (4) a
nonparametric Gaussian Process model of trajectories. We present an MCMC
sampling framework for performing joint inference over recursive activity
descriptions and assignment of trajectories to groups, integrating out
continuous parameters. We demonstrate the model's expressive power in several
simulated and complex real-world scenarios from the VIRAT and UCLA Aerial Event
video data sets.Comment: 10 pages, 6 figures, in Proceedings of the 30th AAAI Conference on
Artificial Intelligence (AAAI'16), Phoenix, AZ, 201
Shaped extensions of singular spectrum analysis
Extensions of singular spectrum analysis (SSA) for processing of
non-rectangular images and time series with gaps are considered. A circular
version is suggested, which allows application of the method to the data given
on a circle or on a cylinder, e.g. cylindrical projection of a 3D ellipsoid.
The constructed Shaped SSA method with planar or circular topology is able to
produce low-rank approximations for images of complex shapes. Together with
Shaped SSA, a shaped version of the subspace-based ESPRIT method for frequency
estimation is developed. Examples of 2D circular SSA and 2D Shaped ESPRIT are
presented
Basic Singular Spectrum Analysis and Forecasting with R
Singular Spectrum Analysis (SSA) as a tool for analysis and forecasting of
time series is considered. The main features of the Rssa package, which
implements the SSA algorithms and methodology in R, are described and examples
of its use are presented. Analysis, forecasting and parameter estimation are
demonstrated by means of case study with an accompanying code in R
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