Comparing gesture recognition accuracy using color and depth information

  • Authors:

  • Paul Doliotis;Alexandra Stefan;Christopher McMurrough;David Eckhard;Vassilis Athitsos

  • Affiliations:

  • University of Texas at Arlington Arlington, Texas;University of Texas at Arlington Arlington, Texas;University of Texas at Arlington Arlington, Texas;University of Texas at Arlington Arlington, Texas;University of Texas at Arlington Arlington, Texas

  • Venue:
  • Proceedings of the 4th International Conference on PErvasive Technologies Related to Assistive Environments
  • Year:
  • 2011

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Abstract

In human-computer interaction applications, gesture recognition has the potential to provide a natural way of communication between humans and machines. The technology is becoming mature enough to be widely available to the public and real-world computer vision applications start to emerge. A typical example of this trend is the gaming industry and the launch of Microsoft's new camera: the Kinect. Other domains, where gesture recognition is needed, include but are not limited to: sign language recognition, virtual reality environments and smart homes. A key challenge for such real-world applications is that they need to operate in complex scenes with cluttered backgrounds, various moving objects and possibly challenging illumination conditions. In this paper we propose a method that accommodates such challenging conditions by detecting the hands using scene depth information from the Kinect. On top of our detector we employ a dynamic programming method for recognizing gestures, namely Dynamic Time Warping (DTW). Our method is translation and scale invariant which is a desirable property for many HCI systems. We have tested the performance of our approach on a digits recognition system. All experimental datasets include hand signed digits gestures but our framework can be generalized to recognize a wider range of gestures.