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Real-time multi-trajectory matching for dynamic hand gesture recognition

机译：实时多轨迹匹配，用于动态手势识别

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摘要

Focus on the field of dynamic gesture recognition, there is a problem of the action, is difficult to recognise when multiple fingertips move in a small range (rotation, grabbing), the authors proposed a method to get the recognition results with high robustness in real-time. Firstly, they proposed the concavity kernel accumulation algorithm (CKA) to cluster corners in an image. Secondly, they deem CKA as a region proposal generator and combined it with convolutional neural network to detect fingertips. Thirdly, they proposed the global nearest neighbour point matching algorithm to match fingertips from two frames. Finally, the long short-term memory is used in multi-trajectory recognition to get the results of gesture recognition. Experiments show that their method could recognise multi-trajectory gestures accurately, furthermore, it can run in real time (20 FPS) without graphics processing unit (GPU).
机译：着眼于动态手势识别领域，存在一个动作问题，当多个指尖在很小的范围内移动（旋转，抓握）时很难识别，作者提出了一种在现实中获得高鲁棒性的识别结果的方法。 -时间。首先，他们提出了凹核累积算法（CKA）对图像中的角进行聚类。其次，他们将CKA视为区域提议生成器，并将其与卷积神经网络相结合以检测指尖。第三，他们提出了全局最近邻点匹配算法来匹配两个帧的指尖。最后，将长时短记忆用于多轨迹识别中以获得手势识别的结果。实验表明，他们的方法可以准确识别多轨迹手势，而且无需图形处理单元（GPU）即可实时运行（20 FPS）。

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