# Gesture Recognition in RGB Videos UsingHuman Body Keypoints and Dynamic Time Warping

arXiv: https://arxiv.org/abs/1906.12171 · HTML: https://mlsanity.com/paper/1906.12171/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jun 2019.

## Built on by (most cited first, 3 of 3)

- [Using Hand Pose Estimation To Automate Open Surgery Training Feedback](https://mlsanity.com/paper/2211.07021/) (2022)
- [Sign and Search: Sign Search Functionality for Sign Language Lexica](https://mlsanity.com/paper/2107.13637/) (2021)
- [Object Recognition in Human Computer Interaction:- A Comparative Analysis](https://mlsanity.com/paper/2411.04263/) (2024)

## Abstract (arXiv)

Gesture recognition opens up new ways for humans to intuitively interact with machines. Especially for service robots, gestures can be a valuable addition to the means of communication to, for example, draw the robot's attention to someone or something. Extracting a gesture from video data and classifying it is a challenging task and a variety of approaches have been proposed throughout the years. This paper presents a method for gesture recognition in RGB videos using OpenPose to extract the pose of a person and Dynamic Time Warping (DTW) in conjunction with One-Nearest-Neighbor (1NN) for time-series classification. The main features of this approach are the independence of any specific hardware and high flexibility, because new gestures can be added to the classifier by adding only a few examples of it. We utilize the robustness of the Deep Learning-based OpenPose framework while avoiding the data-intensive task of training a neural network ourselves. We demonstrate the classification performance of our method using a public dataset.