Diver-Robot Communication Dataset for Underwater Hand Gesture Recognition

TL;DR

This paper introduces a comprehensive underwater diving gesture dataset and compares visual and glove-based recognition methods to enhance human-robot underwater communication.

Contribution

It provides an open dataset of 30,000+ frames with annotated gestures and evaluates the efficiency of visual versus glove-based gesture recognition methods in underwater conditions.

Findings

The dataset includes diverse gestures from multiple divers in different environments.

Glove-based recognition achieves higher accuracy but introduces communication delay.

Visual recognition performance varies with visibility and distance.

Abstract

In this paper, we present a dataset of diving gesture images used for human-robot interaction underwater. By offering this open access dataset, the paper aims at investigating the potential of using visual detection of diving gestures from an autonomous underwater vehicle (AUV) as a form of communication with a human diver. In addition to the image recording, the same dataset was recorded using a smart gesture recognition glove. The glove uses elastomer sensors and on-board processing to determine the selected gesture and transmit the command associated with the gesture to the AUV via acoustics. Although this method can be used under different visibility conditions and even without line of sight, it introduces a communication delay required for the acoustic transmission of the gesture command. To compare efficiency, the glove was equipped with visual markers proposed in a gesture-based language called CADDIAN and recorded with an underwater camera in parallel to the glove's onboard recognition process. The dataset contains over 30,000 underwater frames of nearly 900 individual gestures annotated in corresponding snippet folders. The dataset was recorded in a balanced ratio with five different divers in sea and five different divers in pool conditions, with gestures recorded at 1, 2 and 3 metres from the camera. The glove gesture recognition statistics are reported in terms of average diver reaction time, average time taken to perform a gesture, recognition success rate, transmission times and more. The dataset presented should provide a good baseline for comparing the performance of state of the art visual diving gesture recognition techniques under different visibility conditions.