Hand gesture is one of the most intuitive and natural ways for human to communicate with computers, and it has been widely adopted in many human–computer interaction applications. However, it is still a challenging problem when confronted with complex background, illumination variation and occlusion in real-world scenarios. In this paper, a two-stage hand gesture recognition method is proposed to tackle these problems. At the first stage, hand pose estimation is developed to locate the hand keypoints using the convolutional pose machine, which can effectively localize hand keypoints even in a complex background. At the second stage, the Fuzzy Gaussian mixture models (FGMMs) are tailored to reject the nongesture patterns and classify the gestures based on the estimated hand keypoints. Extensive experiments are conducted to evaluate the performance of the proposed method, and the result demonstrates that the proposed algorithm is effective, robust, and satisfactory in real-time scenarios.

中文翻译：

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基于卷积姿态机和模糊高斯混合模型的复杂背景下手势识别

手势是人类与计算机通信的最直观自然的方法之一，并且已在许多人机交互应用程序中得到广泛采用。但是，在现实情况下，面对复杂的背景，照明变化和遮挡，这仍然是一个充满挑战的问题。本文提出了一种两阶段手势识别方法来解决这些问题。在第一阶段，开发了手部姿势估计以使用卷积姿势机来定位手部关键点，即使在复杂的背景下也可以有效地定位手部关键点。在第二阶段，对模糊高斯混合模型（FGMM）进行定制，以拒绝非手势模式并根据估计的手关键点对手势进行分类。