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Realtime gesture recognition under the multi-layered parallel recognition framework of QVIPS

机译:在QVIPS的多层并行识别框架下实时手势识别

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The importance of gesture protocols has caught little attention in the field of gesture-related computer vision research. The gesture protocols are the consensus on gestures, which are formed through face-to-face communication in our daily lives. Traditional gesture recognition algorithms unilaterally presupposed the gesture protocol without the consensus from users. As a consequence, users were directed to follow the rigidly prescribed gestures unintentionally. Standing firm on the belief that the gestures are not substitute for keyboards and mice, we propose a flexible gesture recognition framework which can adapt to any kinds of gestures presented by the user him/herself. To focus on a process in which the gesture protocols are formed, the Quadruple Visual Interest Point Strategy (QVIPS) is newly introduced. QVIPS enables the system to observe a gesture from multilateral perspectives and to recognize the position, posture and motion information of a user under a unified framework. Inevitably QVIPS requires additional computational burden. We deal with this problem by the combinational use of a Gaussian Density Feature (GDF) and fast Fourier transform (FFT). The proposed system performs recognition in real-time without any special purpose hardware.
机译:手势协议的重要性在姿态相关的计算机视觉研究领域却没有注意。手势协议是手势的共识,这些手势是通过我们日常生活中面对面的通信形成的。传统的手势识别算法单方面预先推出了手势协议,而不会有来自用户的共识。因此,用户旨在无意中遵循刚性规定的手势。常规公司对手势不替代键盘和小鼠的信念,我们提出了一种灵活的手势识别框架,可以适应用户他/她自己所呈现的任何类型的手势。要专注于形成手势协议的过程,新引入了四倍的视觉感兴趣点策略(QVIPS)。 QVIPS使系统能够观察来自多边视角的手势,并在统一的框架下识别用户的位置,姿势和运动信息。不可避免地QVIPS需要额外的计算负担。我们通过组合使用高斯密度特征(GDF)和快速傅里叶变换(FFT)来处理这个问题。建议的系统实时执行识别,而无需任何特殊的用途。

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