This paper presents a real-time pipeline for dynamic arm gesture recognition based on OpenPose keypoint estimation, keypoint normalization, and a recurrent neural network classifier. The 1 x 1 normalization scheme and two feature representations (coordinate- and angle-based) are presented for the pipeline. In addition, an efficient method to improve robustness against camera angle variations is also introduced by using artificially rotated training data. Experiments on a custom traffic-control gesture dataset demonstrate high accuracy across varying viewing angles and speeds. Finally, an approach to calculate the speed of the arm signal (if necessary) is also presented.

翻译：本文提出了一种基于OpenPose关键点估计、关键点归一化与循环神经网络分类器的实时动态手势识别流水线。针对该流水线，提出了1×1归一化方案以及两种特征表示方法（基于坐标与基于角度）。此外，通过使用人工旋转的训练数据，引入了一种提升模型对摄像机角度变化鲁棒性的高效方法。在自定义交通指挥手势数据集上的实验表明，该方法在不同视角与速度下均能实现高精度识别。最后，本文还提出了一种计算手臂信号速度（如必要）的方法。