Automatic gesture recognition for intelligent human-robot interaction
Automatic gesture recognition for intelligent human-robot interaction
复制标题
自动手势识别实现智能人机交互
DOI：
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复制
发表时间：
2006
期刊：
影响因子：
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通讯作者：
Seong
中科院分区：
文献类型：
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作者：
Seong
智能机器人需要与人类进行自然交互。对手势的视觉解读对于实现自然的人机交互（HRI）是有用的。先前的人机交互研究集中在手语、手势语和指令手势识别等问题上。然而，为了使人机交互自然进行，需要对全身手势进行自动识别。这可能是一个具有挑战性的问题，因为从全身手势中描述和构建有意义的手势模式是复杂的任务。本文提出了一种在移动机器人上同时发现和识别全身关键手势的新方法。我们的方法与其他人机交互方法（如语音识别、人脸识别等）同时使用。在这方面，应该考虑执行速度和识别性能。为了高效和自然地操作，我们在手势识别的每个步骤中使用了几种方法；关节信息的学习和提取，将手势表示为聚类序列，利用隐马尔可夫模型发现和识别手势。此外，我们构建了一个大型手势数据库，并用它验证了我们的方法。结果，我们的方法成功地被应用于移动机器人并在其中运行。
An intelligent robot requires natural interaction with humans. Visual interpretation of gestures can be useful in accomplishing natural human-robot interaction (HRl). Previous HRI researches were focused on issues such as hand gesture, sign language, and command gesture recognition. However, automatic recognition of whole body gestures is required in order to operate HRI naturally. This can be a challenging problem because describing and modeling meaningful gesture patterns from whole body gestures are complex tasks. This paper presents a new method for spotting and recognizing whole body key gestures at the same time on a mobile robot. Our method is simultaneously used with other HRI approaches such as speech recognition, face recognition, and so forth. In this regard, both of execution speed and recognition performance should be considered. For efficient and natural operation, we used several approaches at each step of gesture recognition; learning and extraction of articulated joint information, representing gesture as a sequence of clusters, spotting and recognizing a gesture with HMM. In addition, we constructed a large gesture database, with which we verified our method. As a result, our method is successfully included and operated in a mobile robot