Tracking and recognising hand gestures, using statistical shape models
Tracking and recognising hand gestures, using statistical shape models
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使用统计形状模型跟踪和识别手势
DOI：
10.1016/s0262-8856(96)01136-5
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发表时间：
1997
期刊：
影响因子：
--
通讯作者：
Tim Cootes
中科院分区：
文献类型：
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作者：
T. Ahmad;C. Taylor;A. Lanitis;Tim Cootes
从视频图像的手势识别是相当大的兴趣，作为一种手段，提供简单和直观的人机界面。可能的应用范围从取代鼠标作为指向设备到虚拟现实和与聋人的交流。我们描述了一种方法来跟踪图像序列中的一只手，并在每个视频帧中识别它采用的五个手势。一个基于统计的点分布模型（PDM）是用来提供一个紧凑的参数化描述的手的形状的任何手势或它们之间的过渡。所得形状参数的值用于统计分类器中以识别手势。该模型可以作为一个可变形的模板来跟踪通过视频序列的手，但这证明是不可靠的。我们描述了如何一组模型，一个为五个手势，可以用于跟踪自动选择适当的模型。我们表明，这将导致可靠的跟踪和手势识别两个“看不见的”视频序列中，所有的手势都使用。
Hand gesture recognition from video images is of considerable interest as a means of providing simple and intuitive man-machine interfaces. Possible applications range from replacing the mouse as a pointing device to virtual reality and communication with the deaf. We describe an approach to tracking a hand in an image sequence and recognising, in each video frame, which of five gestures it has adopted. A statistically based Point Distribution Model (PDM) is used to provide a compact parametrised description of the shape of the hand for any of the gestures or the transitions between them. The values of the resulting shape parameters are used in a statistical classifier to identify gestures. The model can be used as a deformable template to track a hand through a video sequence but this proves unreliable. We describe how a set of models, one for each of the five gestures, can be used for tracking with the appropriate model selected automatically. We show that this results in reliable tracking and gesture recognition for two ‘unseen’ video sequences in which all the gestures are used.