Discovery and segmentation of activities in video
Brand, M.; Kettnaker, V.
Pattern Analysis and Machine Intelligence, IEEE Transactions on
Volume 22, Issue 8, Aug 2000 Page(s):844 - 851
Digital Object Identifier 10.1109/34.868685
Summary:Hidden Markov models (HMMs) have become the workhorses of the
monitoring and event recognition literature because they bring to
time-series analysis the utility of density estimation and the
convenience of dynamic time warping. Once trained, the internals of
these models are considered opaque; there is no effort to interpret the
hidden states. We show that by minimizing the entropy of the joint
distribution, an HMM's internal state machine can be made to organize
observed activity into meaningful states. This has uses in video
monitoring and annotation, low bit-rate coding of scene activity, and
detection of anomalous behavior. We demonstrate with models of office
activity and outdoor traffic, showing how the framework learns principal
modes of activity and patterns of activity change. We then show how this
framework can be adapted to infer hidden state from extremely ambiguous
images, in particular, inferring 3D body orientation and pose from
sequences of low-resolution silhouettes
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