  Semi-supervised and unsupervised systems provide operators with invaluable
support and can tremendously reduce the operators load. In the light of the
necessity to process large volumes of video data and provide autonomous
decisions, this work proposes new learning algorithms for activity analysis in
video. The activities and behaviours are described by a dynamic topic model.
Two novel learning algorithms based on the expectation maximisation approach
and variational Bayes inference are proposed. Theoretical derivations of the
posterior of model parameters are given. The designed learning algorithms are
compared with the Gibbs sampling inference scheme introduced earlier in the
literature. A detailed comparison of the learning algorithms is presented on
real video data. We also propose an anomaly localisation procedure, elegantly
embedded in the topic modeling framework. The proposed framework can be applied
to a number of areas, including transportation systems, security and
surveillance.
