用无监督模型实时识别手部动态手势，让机器人更懂人类指令。
Dynamic Hand Gesture Recognition for Robot Manipulator Tasks
- 基于高斯混合模型的无监督方法，自动学习手势变化模式。
- 训练与实时测试均实现高精度识别，支持多任务手势输入。
- 适合人机协作场景，尤其适用于工业机器人操作界面。
本文提出一种新方法，用于识别动态手部手势，实现人与机器人之间的无缝交互。每个机器人操作任务对应一个特定手势，任务多样导致手势种类繁多，且存在多种动态变化。通过基于高斯混合模型的无监督方法，系统可实时准确识别所有手势变体。训练与实时测试结果均验证了该方法的有效性。
原文摘要 · Abstract (English)
This paper proposes a novel approach to recognizing dynamic hand gestures facilitating seamless interaction between humans and robots. Here, each robot manipulator task is assigned a specific gesture. There may be several such tasks, hence, several gestures. These gestures may be prone to several dynamic variations. All such variations for different gestures shown to the robot are accurately recognized in real-time using the proposed unsupervised model based on the Gaussian Mixture model. The accuracy during training and real-time testing prove the efficacy of this methodology.
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