Abstract
The number of potential applications has made automatic recognition of human actions a very active research area. Different approaches have been followed based on trajectories through some state space. In this paper we also model an action as a trajectory through a state space, but we represent the actions as a sequence of temporal isolated instances, denoted primitives. These primitives are each defined by four features extracted from motion images. The primitives are recognized in each frame based on a trained classifier resulting in a sequence of primitives. From this sequence we recognize different temporal actions using a probabilistic Edit Distance method. The method is tested on different actions with and without noise and the results show recognition rates of 88.7% and 85.5%, respectively.
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Fihl, P., Holte, M.B., Moeslund, T.B. (2009). Motion Primitives and Probabilistic Edit Distance for Action Recognition. In: Sales Dias, M., Gibet, S., Wanderley, M.M., Bastos, R. (eds) Gesture-Based Human-Computer Interaction and Simulation. GW 2007. Lecture Notes in Computer Science(), vol 5085. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-92865-2_3
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DOI: https://doi.org/10.1007/978-3-540-92865-2_3
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