Year: 2025
Type: conference-paper
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 × 1 normalization scheme and two feature representations (coordinateand 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.
Authors: Milán Zsolt Bagladi, László Gulyás, Gergő Szalay
Institution: Eötvös Loránd University
Language: English
Subfield: Human-Computer Interaction
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4415337591