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This paper presents a distributed system for the recognition of human actions using views of the scene grabbed by different cameras. 2D frame descriptors are extracted for each available view to capture the variability in human motion. These descriptors are projected into a lower dimensional space and fed into a probabilistic classifier to output a posterior distribution of the action performed according to the descriptor computed at each camera. Classifier fusion algorithms are then used to merge the posterior distributions into a single distribution. The generated single posterior distribution is fed into a sequence classifier to make the final decision on the performed activity. The system can instantiate different algorithms for the different tasks, as the interfaces between modules are clearly defined. Results on the classification of the actions in the IXMAS dataset are reported. The accuracy of the proposed system is similar to state-of-the-art 3D methods, even though it uses only well-known 2D pattern recognition techniques and does not need to project the data into a 3D space or require camera calibration parameters.
human action recognition; bayesian networks; computer vision; machine learning