  Discovery and recognition of Group Activities (GA) based on imagery data
processing have significant applications in persistent surveillance systems,
which play an important role in some Internet services. The process is involved
with analysis of sequential imagery data with spatiotemporal associations.
Discretion of video imagery requires a proper inference system capable of
discriminating and differentiating cohesive observations and interlinking them
to known ontologies. We propose an Ontology based GAR with a proper inference
model that is capable of identifying and classifying a sequence of events in
group activities. A multi-layered Hidden Markov Model (HMM) is proposed to
recognize different levels of abstract GA. The multi-layered HMM consists of N
layers of HMMs where each layer comprises of M number of HMMs running in
parallel. The number of layers depends on the order of information to be
extracted. At each layer, by matching and correlating attributes of detected
group events, the model attempts to associate sensory observations to known
ontology perceptions. This paper demonstrates and compares performance of three
different implementation of HMM, namely, concatenated N-HMM, cascaded C-HMM and
hybrid H-HMM for building effective multi-layered HMM.
