  This paper uses Factored Latent Analysis (FLA) to learn a factorized,
segmental representation for observations of tracked objects over time.
Factored Latent Analysis is latent class analysis in which the observation
space is subdivided and each aspect of the original space is represented by a
separate latent class model. One could simply treat these factors as completely
independent and ignore their interdependencies or one could concatenate them
together and attempt to learn latent class structure for the complete
observation space. Alternatively, FLA allows the interdependencies to be
exploited in estimating an effective model, which is also capable of
representing a factored latent state. In this paper, FLA is used to learn a set
of factored latent classes to represent different modalities of observations of
tracked objects. Different characteristics of the state of tracked objects are
each represented by separate latent class models, including normalized size,
normalized speed, normalized direction, and position. This model also enables
effective temporal segmentation of these sequences. This method is data-driven,
unsupervised using only pairwise observation statistics. This data-driven and
unsupervised activity classi- fication technique exhibits good performance in
multiple challenging environments.
