手势
计算机科学
手势识别
人机交互
背景(考古学)
人工智能
光学(聚焦)
计算机视觉
生物
光学
物理
古生物学
作者
Sigal Berman,Helman I. Stern
出处
期刊:IEEE transactions on systems, man and cybernetics
[Institute of Electrical and Electronics Engineers]
日期:2011-08-15
卷期号:42 (3): 277-290
被引量:102
标识
DOI:10.1109/tsmcc.2011.2161077
摘要
A gesture recognition system (GRS) is comprised of a gesture, gesture-capture device (sensor), tracking algorithm (for motion capture), feature extraction, and classification algorithm. With the impending movement toward natural communication with mechanical and software systems, it is important to examine the first apparatus that separates the human communicator and the device being controlled. Although there are numerous reviews of GRSs, a comprehensive analysis of the integration of sensors into GRSs and their impact on system performance is lacking in the professional literature. Thus, we have undertaken this effort. Determination of the sensor stimulus, context of use, and sensor platform are major preliminary design issues in GRSs. Thus, these three components form the basic structure of our taxonomy. We emphasize the relationship between these critical components and the design of the GRS in terms of its architectural functions and computational requirements. In this treatise, we consider sensors that are capable of capturing dynamic and static arm and hand gestures. Although we discuss various sensor types, our main focus is on visual sensors as we expect these to become the sensor of choice in the foreseeable future. We delineate the challenges ahead for their increased effectiveness in this application domain. We note as a special challenge, the development of sensors that take over many of the functions the GRS designer struggles with today. We believe our contribution, in this first survey on sensors for GRSs, can give valuable insights into this important research and development topic, and encourage advanced research directions and new approaches.
科研通智能强力驱动
Strongly Powered by AbleSci AI