Human movement analysis through vision sensing systems is an important subject regarding Human-Robot interaction. This is a growing area of research, with wide range of aplications fields. The ability to recognize human actions using passive sensing modalities, is a decisive factor for machine interaction. In mobile platforms, image processing is regarded as a problem, due to constant changes. We propose an approach, based on Horopter technique, to extract Regions Of Interest (ROI) delimiting human contours. This fact will allow tracking algorithms to provide faster and accurate responses to human feature extraction. The key features are head and both hand positions, that will be tracked within image context. Posterior to feature acquisition, they will be contextualized within a technique, Laban Movement Analysis (LMA) and will be used to provide sets of classifiers. The implementation of the LMA techquine will be based on Bayesian Networks. We will use these bayesian classifiers to label/classify human emotion within the context of expressive movements. Compared to full image tracking, results improved with the implemented approach, the horopter and consequently so did classification results.
['Luis Santos', 'José Augusto Prado', 'Jorge Dias']
Human Robot interaction studies on laban human movement analysis and dynamic background segmentation