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Summary |
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activities at IST campus The
project ARGUS aims at developing
methods for the analysis and recognition of human activities using models learned from the video data in an
automatic way. The
project will develop a new representation for activity recognition. We assume
that the (person, vehicle, animal) trajectory is generated by a set of
space-varying velocity fields learned from the video data. Different velocity
fields correspond to different motion regimes. Consider, for example, a cross
between two streets. We may have one velocity field describing the pedestrian
motion in the first street and a second velocity field in the second street.
Switching between the driving fields is possible and the switching
probabilities are also space-varying (they depend on the person position in
the scene); it’s possible to switch at the cross, but not far from it.
Therefore, switching is described by a field of space-varying stochastic
matrices. This
model will be learned from a set of observed trajectories using the
expectation-maximization (EM) algorithm. This seems to be a natural choice,
since we do not know which velocity field is driving the motion (active
field); these active field labels are thus treated as missing data. The
estimation of the space-varying matrices will be performed in an information
theoretic framework using tools from differential geometry (natural gradient
based on Riemaniann metric). This will be done in
an unsupervised way. The number of models, the velocity fields and the field
of switching matrices will all be learned from unlabeled data (video
sequences). The proposed model is intuitive and easily understandable/
interpretable since each velocity field can be observed and describes a
different type of motion in the scene. This information can be used by the
manager of the infrastructure to characterize the typical ways in which
people move in that place. Furthermore, it is also a good starting point for
activity recognition based on the sequence of active models (switching
sequence) and the computation of the sequence probability in order to detect
abnormal behaviors. |
