TUTCRIS - Tampereen teknillinen yliopisto


View-invariant action recognition based on Artificial Neural Networks



JulkaisuIEEE Transactions on Neural Networks and Learning Systems
DOI - pysyväislinkit
TilaJulkaistu - 2012
OKM-julkaisutyyppiA1 Alkuperäisartikkeli


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 (SOM). 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.