Dynamic Bayesian networks for visual recognition of dynamic gestures

Issue title: IBERAMIA '02

Article type: Research Article

Authors: Avilés-Arriaga, Héctor Hugo | Sucar, Luis Enrique

Affiliations: Tec de Monterrey, Campus Cuernavaca, Av. Paseo de la Reforma No. 182-A Col. Lomas de Cuernavaca, C.P. 82589 Cuernavaca, Morelos, México. Tel.: +52 73 29 71 69; E-mail: 00374765@academ01.mor.itesm.mx, esucar@itesm.mx

Abstract: Dynamic Bayesian networks are a powerful representation to describe processes that vary over time inside a stochastic framework. This paper describes an online visual recognition system to recognize a set of five dynamic gestures executed with the user's right hand using dynamic Bayesian networks for recognition. Gestures are oriented to command mobile robots. The system employs a radial scan segmentation algorithm combined with a statistical-based skin detection method to find the candidate face of the user and to track his right-hand. It uses four simple features to describe the user's right-hand movement. Our system is able to recognize these five gestures in real-time with an average recognition rate of 84.01%, better result than using hidden Markov models for recognition.

Keywords: Dynamic Bayesian networks, hidden Markov models, gesture recognition

Journal: Journal of Intelligent & Fuzzy Systems, vol. 12, no. 3-4, pp. 243-250, 2002