Recognition of Visual Activities and Interactions by Stochastic Parsing
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Karlsruhe-Verbmobil Speech Recognition Engine
ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97) -Volume 1 - Volume 1
Segmenting motion capture data into distinct behaviors
GI '04 Proceedings of the 2004 Graphics Interface Conference
Learning Functional Object-Categories from a Relational Spatio-Temporal Representation
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
Learning and generalization of motor skills by learning from demonstration
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Finding motion primitives in human body gestures
GW'05 Proceedings of the 6th international conference on Gesture in Human-Computer Interaction and Simulation
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In robotics research is an increasing need for knowledge about human motions. However humans tend to perceive motion in terms of discrete motion primitives. Most systems use data-driven motion segmentation to retrieve motion primitives. Besides that the actual intention and context of the motion is not taken into account. In our work we propose a procedure for segmenting motions according to their functional goals, which allows a structuring and modeling of functional motion primitives. The manual procedure is the first step towards an automatic functional motion representation. This procedure is useful for applications such as imitation learning and human motion recognition. We applied the proposed procedure on several motion sequences and built a motion recognition system based on manually segmented motion capture data. We got a motion primitive error rate of 0.9% for the marker-based recognition. Consequently the proposed procedure yields motion primitives that are suitable for human motion recognition.