Abstract
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.
Abstract (translated)
本文提出了一种基于OpenPose关键点估计、关键点归一化和循环神经网络分类器的实时动态手臂手势识别流水线。该流水线提出了1x1归一化方案以及两种特征表示（基于坐标的和基于角度的）。此外，通过使用人工旋转的训练数据，还介绍了一种提高对抗相机角度变化鲁棒性的有效方法。在自定义交通控制手势数据集上的实验表明，在不同的观察角度和速度下具有高准确性。最后，本文还提出了一种计算手臂信号速度（如有必要）的方法。
URL
https://arxiv.org/abs/2509.25042
