NDLI: Recognizing complex, parameterized gestures from monocular image sequences

Content Provider	IEEE Xplore Digital Library
Author	Axenbeck, T. Bennewitz, M. Behnke, S. Burgard, W.
Copyright Year	2008
Description	Author affiliation: Inst. for Comput. Sci., Univ. of Bonn, Bonn (Behnke, S.) \|\| Inst. for Comput. Sci., Univ. of Freiburg, Freiburg (Axenbeck, T.; Bennewitz, M.; Burgard, W.)
Abstract	Robotic assistants designed to coexist and communicate with humans in the real world should be able to interact with them in an intuitive way. This requires that the robots are able to recognize typical gestures performed by humans such as head shaking/nodding, hand waving, or pointing. In this paper, we present a system that is able to spot and recognize complex, parameterized gestures from monocular image sequences. To represent people, we locate their faces and hands using trained classifiers and track them over time. We use few, expressive features extracted out of this compact representation as input to hidden Markov models (HMMs). First, we segment gestures into distinct phases and train HMMs for each phase separately. Then, we construct composed HMMs, which consist of the individual phase-HMMs. Once a specific phase is recognized, we estimate the parameter of the current gesture, e.g., the target of a pointing gesture. As we demonstrate in the experiments, our method is able to robustly locate and track hands, despite of the fact that they can take a large number of substantially different shapes. Based on this, our system is able to reliably spot and recognize a variety of complex, parameterized gestures.
Starting Page	687
Ending Page	692
File Size	3083920
Page Count	6
File Format	PDF
ISBN	9781424428212
DOI	10.1109/ICHR.2008.4755973
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-12-01
Publisher Place	South Korea
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image segmentation Image recognition Head Parameter estimation Target recognition Phase estimation Hidden Markov models Feature extraction Image sequences Human robot interaction
Content Type	Text
Resource Type	Article

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