Decomposition of human motion into dynamics-based primitives with application to drawing tasks

Using tools from dynamical systems and systems identification, we develop a framework for the study of primitives for human motion, which we refer to as movemes. The objective is understanding human motion by decomposing it into a sequence of elementary building blocks that belong to a known alphabet of dynamical systems. We develop a segmentation and classification algorithm in order to reduce a complex activity into the sequence of movemes that have generated it. We test our ideas on data sampled from five human subjects who were drawing figures using a computer mouse. Our experiments show that we are able to distinguish between movemes and recognize them even when they take place in activities containing an unspecified number of movemes.

[1]  Takeo Kanade,et al.  Introduction to the Special Section on Video Surveillance , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  J R Flanagan,et al.  The Role of Internal Models in Motion Planning and Control: Evidence from Grip Force Adjustments during Movements of Hand-Held Loads , 1997, The Journal of Neuroscience.

[3]  E. Bizzi,et al.  Linear combinations of primitives in vertebrate motor control. , 1994, Proceedings of the National Academy of Sciences of the United States of America.

[4]  Pietro Perona,et al.  Visual Input for Pen-Based Computers , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  Jessica K. Hodgins,et al.  Tracking and Modifying Upper-body Human Motion Data with Dynamic Simulation , 1999, Computer Animation and Simulation.

[6]  Luiz Velho,et al.  A New Interface Paradigm for Motion Capture Based Animation Systems , 1997, Computer Animation and Simulation.

[7]  Ferdinando A. Mussa-Ivaldi,et al.  Toward a neurobiology of coordinate transformations , 1995 .

[8]  Larry S. Davis,et al.  Recognizing Human Facial Expressions From Long Image Sequences Using Optical Flow , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[9]  Aaron F. Bobick,et al.  Learning visual behavior for gesture analysis , 1995, Proceedings of International Symposium on Computer Vision - ISCV.

[10]  Ivan Laptev,et al.  Tracking of Multi-state Hand Models Using Particle Filtering and a Hierarchy of Multi-scale Image Features , 2001, Scale-Space.

[11]  A. Willsky,et al.  A generalized likelihood ratio approach to the detection and estimation of jumps in linear systems , 1976 .

[12]  Pierre Wellner.  The DigitalDesk calculator: tangible manipulation on a desk top display , 1991, UIST '91.

[13]  Pietro Perona,et al.  Reach out and touch space (motion learning) , 1998, Proceedings Third IEEE International Conference on Automatic Face and Gesture Recognition.

[14]  Pietro Perona,et al.  Monocular tracking of the human arm in 3D , 1995, Proceedings of IEEE International Conference on Computer Vision.

[15]  Marc Lavielle,et al.  Optimal segmentation of random processes , 1998, IEEE Trans. Signal Process..

[16]  Fredrik Gustafsson,et al.  Adaptive filtering and change detection , 2000 .

[17]  A. Pedotti,et al.  Postural synergies in axial movements: short and long-term adaptation , 2004, Experimental Brain Research.

[18]  P. Perona,et al.  Classification of human actions into dynamics based primitives with application to drawing tasks , 2003, 2003 European Control Conference (ECC).

[19]  Mitsuo Kawato,et al.  Internal models for motor control and trajectory planning , 1999, Current Opinion in Neurobiology.

[20]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[21]  Vladimir Pavlovic,et al.  Impact of dynamic model learning on classification of human motion , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[22]  Nicola J. Ferrier,et al.  Multidimensional motion segmentation and identification , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[23]  Christoph Bregler,et al.  Learning and recognizing human dynamics in video sequences , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[24]  Sebastian Thrun,et al.  Template-Based Recognition of Pose and Motion Gestures On a Mobile Robot , 1998, AAAI/IAAI.

[25]  Biing-Hwang Juang,et al.  Fundamentals of speech recognition , 1993, Prentice Hall signal processing series.

[26]  Heekuck Oh,et al.  Neural Networks for Pattern Recognition , 1993, Adv. Comput..