Learning context models for the recognition of scenarios

This paper addresses the problem of automatic learning of scenarios. A ubiquitous computing environment must have the ability to perceive its occupants and their activities in order to recognize a context and to provide appropriate services. A context (a scenario) can be modeled as a temporal sequence of situations. Hard coding contexts by hand is a complex task. Our goal is to learn these context models based on a set of videos showing actors playing predefined scenarios. Once these models are learned, we can use them to classify new scenarios. Hidden Markov Models (HMMs) are particularly well suited for problems with a strong temporal structure; they are easily adaptable to variability of input and robust to noise. But two problems need to be addressed: how many HMMs do we need for all possible scenarios and how many states for each HMM. We propose in this paper an approach based on an incremental algorithm addressing these two problems. Under the best conditions we obtained the minimal error rate of 1.96% (2 errors in 102 validation entries).