In
this paper, a novel view invariant action recognition method based on neural
network representation and recognition is proposed. The novel representation of
action videos is based on learning spatially related human body posture
prototypes using self organizing maps. Fuzzy distances from human body posture
prototypes are used to produce a time invariant action representation.
Multilayer perceptrons are used for action classification. The algorithm is
trained using data from a multi-camera setup. An arbitrary number of cameras
can be used in order to recognize actions using a Bayesian framework. The
proposed method can also be applied to videos depicting interactions between
humans, without any modification. The use of information captured from
different viewing angles leads to high classification performance. The proposed
method is the first one that has been tested in challenging experimental
setups, a fact that denotes its effectiveness to deal with most of the open
issues in action recognition.
Keywords: IEEE Project Title 2012, Networking Title, Cloud Computing Title, Wireless Communication Title.
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