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

## TL;DR

This paper introduces a flexible, hardware-independent gesture recognition method for RGB videos that combines OpenPose for pose estimation with Dynamic Time Warping and 1-Nearest-Neighbor for classification, demonstrated on a public dataset.

## Contribution

The paper proposes a novel gesture recognition approach that leverages OpenPose and DTW/1NN, avoiding extensive training and enabling easy addition of new gestures.

## Key findings

- High classification accuracy demonstrated on a public dataset
- Method is hardware-independent and easily extendable with new gestures
- Avoids the need for training neural networks, reducing data requirements

## Abstract

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.