Adaptation to Gesture Performers by an On-Line Teaching System for Spotting Recognition of Gestures from a Time-Varying Image

摘要

We are trying to realize gesture recognition from motion images that capture human gestures and hand mo- tions without the need for tactile sensors or markers, such as data gloves. We have achieved real-time spotting-based recognition with the continuous DP system without any gesture Performers having to be aware of the commence- ment and ending of a motion. Continuous DP is beneficial due to its ability to create a model from a single-gesture motion image sequence. Therefore, even if gesture motions depend on performers, gestures can be recognized after a single instruction. However, to instruct a model, our con- ventional real-time recognition systems require setting of the threshold for recognition as well as segmentation of the model off line. Therefore, we propose in this paper an automatic model segmentation method and a recognition method to allow constructing Performer-adaptive on-line teaching systems. We will also demonstrate the usefulness of this method through evaluative experiments.