Group Behavior Recognition using Dynamic Bayesian Networks

In this PhD thesis we analyze the concepts involved
in the decision making of groups of agents and apply
these concepts in creating a framework for performing
group behavior recognition. We present an overview of
the intention theory, as studied by some great
theorists such as Searle, Bratmann and Cohen, and
show the link with more recent researches. We study
the advantages and drawbacks of some techniques in
the domain and create a new model for representing
and detecting group behaviors, the aim being to
create a unified approach of the problem. Most of
this thesis is consecrated in the detailed
presentation of the model as well as the algorithm
responsible for behavior recognition. Our model is
tested on two different applications involving human
gesture analysis and multimodal fusion of audio and
video data. By means of these applications, we
advance the argument that multivariate sets of
correlated data can be efficiently analyzed under a
unified framework of behavior recognition. We show
that the correlation between different sets of data
can be modeled as cooperation inside a team and that
behavior recognition is a modern approach of
classification and pattern recognition.