Visual-based posture recognition using hybrid neural networks.
Andrea Corradini, Hans-Joachim Böhme, Horst–Michael Groß · 1999
Abstract. This paper describes the preliminary results of the research work currently ongoing at our department and carried out as part of a project founded by the Commission of the European Union. In this paper a novel approach tohuman posture analysis and recognition using standard image processing techniques as well as hybrid neural information processing is presented. We rst develop a reliable and robust person localization module via a combination of oriented lters and threedimensional dynamic neural elds. Then we focus on the view-based recognition of the user's static gestural instructions from a prede ned vocabulary based on both a skin color model and statistical normalized moment invariants. The segmentation of the postures occurs by means of the skin color model based on the Mahalanobis metric. From the resulting binary image containing only regions which have been classi ed as skin candidates we extract translation and scale invariant moments. They are used as input for two di erent neural classi ers whose results are then compared. To train and test the neural classi ers we gathered the data from ve people performing 18 repetitions of eachof ve postures (our vocabulary): stop, go left, go right, hello left and hello right. The system is currently under development with constant updates and new developments. It uses input from a color video camera and is user-independent. The aim is to build a real-time system able to deal with dynamic gestures. 1.