  In the recent years, the desire and need to understand sequential data has
been increasing, with particular interest in sequential contexts such as
patient monitoring, understanding daily activities, video surveillance, stock
market and the like. Along with the constant flow of data, it is critical to
classify and segment the observations on-the-fly, without being limited to a
rigid number of classes. In addition, the model needs to be capable of updating
its parameters to comply with possible evolutions. This interesting problem,
however, is not adequately addressed in the literature since many studies focus
on offline classification over a pre-defined class set. In this paper, we
propose a principled solution to this gap by introducing an adaptive online
system based on Markov switching models with hierarchical Dirichlet process
priors. This infinite adaptive online approach is capable of segmenting and
classifying the sequential data over unlimited number of classes, while meeting
the memory and delay constraints of streaming contexts. The model is further
enhanced by introducing a learning rate, responsible for balancing the extent
to which the model sustains its previous learning (parameters) or adapts to the
new streaming observations. Experimental results on several variants of
stationary and evolving synthetic data and two video datasets, TUM Assistive
Kitchen and collatedWeizmann, show remarkable performance in segmentation and
classification, particularly for evolutionary sequences with changing
distributions and/or containing new, unseen classes.
