Abstract
In this paper we present a novel method called Temporal Nearest End-Effectors (TNEE) to automatically classify full-body human actions captured in real-time. This method uses a simple representation for modeling actions based exclusively on the recent positions of the user’s end-effectors, i.e. hands, head and feet, relative to the pelvis. With this method, the essential information of full-body movements is retained in a reduced form. The recognition procedure combines the evaluation of the performed poses and the temporal coherence. The performance of TNEE is tested with real motion capture data obtaining satisfactory results for real-time applications.
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Mena, O., Unzueta, L., Sierra, B., Matey, L. (2008). Temporal Nearest End-Effectors for Real-Time Full-Body Human Actions Recognition. In: Perales, F.J., Fisher, R.B. (eds) Articulated Motion and Deformable Objects. AMDO 2008. Lecture Notes in Computer Science, vol 5098. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-70517-8_26
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DOI: https://doi.org/10.1007/978-3-540-70517-8_26
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-70516-1
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