Underwater Motion and Activity Recognition using Acoustic Wireless Networks

Hu, Haowei; Sun, Zhi; Lu, Shan

doi:10.1109/icc40277.2020.9148998

Cited by 4 publications

(2 citation statements)

References 9 publications

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“…At the same time, research has also focused on detecting the number of divers present in the view of the AUV camera in various underwater environments [34], [37]. Researchers have also addressed the diver arm motion recognition problem using wireless acoustic networks in a simulated underwater environment [38]; however, there is no real-world data for validation. Furthermore, these networks might not always be available for implementation in the ocean environments.…”

Section: Related Workmentioning

confidence: 99%

DARE: Diver Action Recognition Encoder for Underwater Human–Robot Interaction

2023

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With the growth of sensing, control and robotic technologies, autonomous underwater vehicles (AUVs) have become useful assistants to human divers for performing various underwater operations. In the current practice, the divers are required to carry expensive, bulky, and waterproof keyboards or joystick-based controllers for the supervision and control of AUVs. Therefore, diver action-based supervision is becoming increasingly popular because it is convenient, easier to use, faster, and cost effective. However, various environmental, diver, and sensing uncertainties make the underwater diver action recognition problem challenging. In this regard, this paper presents DARE, a diver action recognition encoder, which is robust to underwater uncertainties and classifies various diver actions including sixteen gestures and three poses with high accuracy. DARE is based on the fusion of stereo-pairs of underwater camera images using bi-channel convolutional layers for feature extraction followed by a systematically designed decision tree of neural network classifiers. DARE is trained using the Cognitive Autonomous Diving Buddy (CADDY) dataset, which consists of a rich set of images of different diver actions in real underwater environments. DARE requires only a few milliseconds to classify one stereo-pair, thus making it suitable for real-time implementation. The results show that DARE achieves up to 95.87% overall accuracy and 92% minimum class accuracy, thus verifying its robustnesss and reliability. Furthermore, a comparative evaluation against existing deep transfer learning architectures reveals that DARE improves the performance of baseline classifiers by up to 3.44% in the overall accuracy and 30% in the minimum class accuracy.INDEX TERMS Autonomous underwater vehicles, diver action recognition, human-robot interaction, bichannel convolutional neural networks, transfer learning.

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Section: Related Workmentioning

confidence: 99%

DARE: Diver Action Recognition Encoder for Underwater Human–Robot Interaction

2023

View full text Add to dashboard Cite

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“…At the same time, research has also focused on detecting the number of divers present in the view of the AUV camera in various underwater environments [24], [27]. Researchers have also addressed the diver arm motion recognition problem using wireless acoustic networks in a simulated underwater environment [28]; however, there is no real-world data for validation. Furthermore, these networks might not always be available for implementation in the ocean environments.…”

Section: Related Workmentioning

confidence: 99%

DARE: AI-based Diver Action Recognition System using Multi-Channel CNNs for AUV Supervision

Yang¹,

Wilson²,

Gupta³

2020

Preprint

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With the growth of sensing, control and robotic technologies, autonomous underwater vehicles (AUVs) have become useful assistants to human divers for performing various underwater operations. In the current practice, the divers are required to carry expensive, bulky, and waterproof keyboards or joystick-based controllers for supervision and control of AUVs. Therefore, diver action-based supervision is becoming increasingly popular because it is convenient, easier to use, faster, and cost effective. However, the various environmental, diver and sensing uncertainties present underwater makes it challenging to train a robust and reliable diver action recognition system. In this regard, this paper presents DARE, a diver action recognition system, that is trained based on Cognitive Autonomous Driving Buddy (CADDY) dataset, which is a rich set of data containing images of different diver gestures and poses in several different and realistic underwater environments. DARE is based on fusion of stereo-pairs of camera images using a multi-channel convolutional neural network supported with a systematically trained tree-topological deep neural network classifier to enhance the classification performance. DARE is fast and requires only a few milliseconds to classify one stereo-pair, thus making it suitable for real-time underwater implementation. DARE is comparatively evaluated against several existing classifier architectures and the results show that DARE supersedes the performance of all classifiers for diver action recognition in terms of overall as well as individual class accuracies and F1-scores.

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Poster: AquaGuard: A Sonar-based Pool Monitoring System

Hou,

Zheng,

et al. 2024

Proceedings of the 22nd ACM Conference on Embedded Networked Sensor Systems

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Underwater Motion and Activity Recognition using Acoustic Wireless Networks

Cited by 4 publications

References 9 publications

DARE: Diver Action Recognition Encoder for Underwater Human–Robot Interaction

DARE: Diver Action Recognition Encoder for Underwater Human–Robot Interaction

DARE: AI-based Diver Action Recognition System using Multi-Channel CNNs for AUV Supervision

Poster: AquaGuard: A Sonar-based Pool Monitoring System

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