Arm-Hand Behaviours Modelling: From Attention to Imitation
We present a new and original method for modelling arm-hand actions, learning and recognition. We use an incremental approach to separate the arm-hand action recognition problem into three levels. The lower level exploits bottom-up attention to select the region of interest, and attention is specifically tuned towards human motion. The middle level serves to classify action primitives exploiting motion features as descriptors. Each of the primitives is modelled by a Mixture of Gaussian, and it is recognised by a complete, real time and robust recognition system. The higher level system combines sequences of primitives using deterministic finite automata. The contribution of the paper is a compositional based model for arm-hand behaviours allowing a robot to learn new actions in a one time shot demonstration of the action execution.
Keywordsgesture recognition action segmentation human motion analysis
Unable to display preview. Download preview PDF.
- 9.Forsyth, D.A., Arikan, O., Ikemoto, L., O’Brien, J.F., Ramanan, D.: Computational studies of human motion: Part 1, tracking and motion synthesis. Foundations and Trends in Computer Graphics and Vision 1(2/3) (2005)Google Scholar
- 10.Krüger, V., Kragic, D., Geib, C.: The meaning of action a review on action recognition and mapping. Advanced Robotics 21, 1473–1501 (2007)Google Scholar
- 12.Gabor, D.: Theory of communication. J. IEE 93(26, Part III), 429–460 (1946)Google Scholar
- 14.Jones, J.P., Palmer, L.A.: An evaluation of the two-dimensional gabor filter model of simple receptive fields in cat striate cortex. Journal of Neurophysiology 58, 1233–1258 (1987)Google Scholar
- 20.Lucas, B.D., Kanade, T.: An iterative image registration technique with an application to stereo vision. In: Proc. of DARPA Imaging Understanding Work, pp. 121–130 (1981)Google Scholar
- 25.Oncina, J., García, P.: Identifying regular languages in polynomial time. World Scientific Publishing, Singapore (1992)Google Scholar