Abstract
Most of the action recognition methods presented in the literature cannot be applied to real life situations. Some of them demand expensive feature extraction or classification processes, some require previous knowledge about starting and ending action times, others are just not scalable. In this paper, we present a real time action recognition method that uses information about the variation of the silhouette shape, which can be extracted and processed with little computational effort, and we apply a fast configuration of lightweight classifiers. The experiments are conducted on theWeizmann dataset and show that our method achieves the state-of-the-art accuracy in real time and can be scaled to work on different conditions and be applied several times simultaneously.
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de Alcântara, M.F., Moreira, T.P., Pedrini, H. (2013). Motion Silhouette-Based Real Time Action Recognition. In: Ruiz-Shulcloper, J., Sanniti di Baja, G. (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2013. Lecture Notes in Computer Science, vol 8259. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41827-3_59
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DOI: https://doi.org/10.1007/978-3-642-41827-3_59
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