Fast gesture recognition based on a two-level representation

Authors:
J. P. Bandera;R. Marfil;A. Bandera;J. A. Rodríguez;L. Molina-Tanco;F. Sandoval
Affiliations:
Grupo ISIS, Dpto. Tecnología Electrónica, E.T.S.I. Telecomunicación, Universidad de Málaga, Campus de Teatinos s/n, 29071 Málaga, Spain;Grupo ISIS, Dpto. Tecnología Electrónica, E.T.S.I. Telecomunicación, Universidad de Málaga, Campus de Teatinos s/n, 29071 Málaga, Spain;Grupo ISIS, Dpto. Tecnología Electrónica, E.T.S.I. Telecomunicación, Universidad de Málaga, Campus de Teatinos s/n, 29071 Málaga, Spain;Grupo ISIS, Dpto. Tecnología Electrónica, E.T.S.I. Telecomunicación, Universidad de Málaga, Campus de Teatinos s/n, 29071 Málaga, Spain;Grupo ISIS, Dpto. Tecnología Electrónica, E.T.S.I. Telecomunicación, Universidad de Málaga, Campus de Teatinos s/n, 29071 Málaga, Spain;Grupo ISIS, Dpto. Tecnología Electrónica, E.T.S.I. Telecomunicación, Universidad de Málaga, Campus de Teatinos s/n, 29071 Málaga, Spain
Venue:
Pattern Recognition Letters
Year:
2009

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Abstract

Towards developing an interface for human-robot interaction, this paper proposes a two-level approach to recognise gestures which are composed of trajectories followed by different body parts. In a first level, individual trajectories are described by a set of key-points. These points are chosen as the corners of the curvature function associated to the trajectory, which will be estimated using and adaptive, non-iterative scheme. This adaptive representation allows removing noise while preserving detail in curvature at different scales. In a second level, gestures are characterised through global properties of the trajectories that compose them. Gesture recognition is performed using a confidence value that integrates both levels. Experimental results show that the performance of the proposed method is high in terms of computational cost and memory consumption, and gesture recognition ability.