Dynamic Time Warping for Off-Line Recognition of a Small Gesture Vocabulary
RATFG-RTS '01 Proceedings of the IEEE ICCV Workshop on Recognition, Analysis, and Tracking of Faces and Gestures in Real-Time Systems (RATFG-RTS'01)
Hand gesture recognition using depth data
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Dynamic gesture vocabulary design for intuitive human-robot dialog
HRI '12 Proceedings of the seventh annual ACM/IEEE international conference on Human-Robot Interaction
Real-time human pose recognition in parts from single depth images
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Spatially unconstrained, gesture-based human-robot interaction
Proceedings of the 8th ACM/IEEE international conference on Human-robot interaction
Spatially unconstrained, gesture-based human-robot interaction
Proceedings of the 8th ACM/IEEE international conference on Human-robot interaction
Human-robot interaction through 3D vision and force control
Proceedings of the 2014 ACM/IEEE international conference on Human-robot interaction
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To achieve an improved human-robot interaction it is necessary to allow the human participant to interact with the robot in a natural way. In this work, a gesture recognition algorithm, based on dynamic time warping, was implemented with a use-case scenario of natural interaction with a mobile robot. Inputs are gesture trajectories obtained using a Microsoft Kinect sensor. Trajectories are stored in the person's frame of reference. Furthermore, the recognition is position-invariant, meaning that only one learned sample is needed to recognize the same gesture performed at another position in the gestural space. In experiments, a set of gestures for a robot waiter was used to train the gesture recognition algorithm. The experimental results show that the proposed modifications of the standard gesture recognition algorithm improve the robustness of the recognition.