基于运动数据流的在线人类手势识别

Xin Zhao, Xue Li, C. Pang, Xiaofeng Zhu, Quan Z. Sheng
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引用次数: 92

摘要

在线人体手势识别在计算机视觉特别是人机交互领域有着广泛的应用。近年来,高性价比的深度相机的出现,为肢体动作手势识别的研究带来了新的趋势。然而,有两个主要的挑战:i)如何从未分割的流中连续识别手势,ii)如何区分相同手势的不同风格和其他类型的手势。本文提出了一种新的高效有效的特征提取方法,该方法采用动态匹配的方法为每一帧构造特征向量,提高了对不同手势特征的敏感性,降低了对同一类手势特征的敏感性。我们在MSRC-12 Kinect Gesture和MSR-Action3D数据集上的综合实验证明了比最先进的方法更优越的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Online human gesture recognition from motion data streams
Online human gesture recognition has a wide range of applications in computer vision, especially in human-computer interaction applications. Recent introduction of cost-effective depth cameras brings on a new trend of research on body-movement gesture recognition. However, there are two major challenges: i) how to continuously recognize gestures from unsegmented streams, and ii) how to differentiate different styles of a same gesture from other types of gestures. In this paper, we solve these two problems with a new effective and efficient feature extraction method that uses a dynamic matching approach to construct a feature vector for each frame and improves sensitivity to the features of different gestures and decreases sensitivity to the features of gestures within the same class. Our comprehensive experiments on MSRC-12 Kinect Gesture and MSR-Action3D datasets have demonstrated a superior performance than the stat-of-the-art approaches.
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