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arXiv · 2509.25042

Fast Real-Time Pipeline for Robust Arm Gesture Recognition

Milán Zsolt Bagladi·László Gulyás·Gergő Szalay
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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.

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Milán Zsolt Bagladi, László Gulyás, Gergő Szalay. 2025-09-29. Fast Real-Time Pipeline for Robust Arm Gesture Recognition. https://doi.org/10.1145/3759355.3759633

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