Temporal Nearest End-Effectors for Real-Time Full-Body Human Actions Recognition

Authors:
Oscar Mena;Luis Unzueta;Basilio Sierra;Luis Matey
Affiliations:
CEIT and Tecnun, University of Navarra, Donostia-San Sebastián, Spain 20018;CEIT and Tecnun, University of Navarra, Donostia-San Sebastián, Spain 20018;Computer Engineering Faculty, University of the Basque Country, Donostia-San Sebastián, Spain 20018;CEIT and Tecnun, University of Navarra, Donostia-San Sebastián, Spain 20018
Venue:
AMDO '08 Proceedings of the 5th international conference on Articulated Motion and Deformable Objects
Year:
2008

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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.