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Pregled bibliografske jedinice broj: 1065217

Stereo-vision based diver pose estimation using LSTM recurrent neural networks for AUV navigation guidance

Chavez, Arturo Gomez; Mueller, Christian A.; Birk, Andreas; Babic, Anja; Miskovic, Nikola
Stereo-vision based diver pose estimation using LSTM recurrent neural networks for AUV navigation guidance // OCEANS 2017 Aberdeen Online Proceedings
Aberdeen: Institute of Electrical and Electronics Engineers (IEEE), 2017. str. 1-6 doi:10.1109/oceanse.2017.8085020 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)

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Naslov
Stereo-vision based diver pose estimation using LSTM recurrent neural networks for AUV navigation guidance

Autori
Chavez, Arturo Gomez ; Mueller, Christian A. ; Birk, Andreas ; Babic, Anja ; Miskovic, Nikola

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
OCEANS 2017 Aberdeen Online Proceedings / - Aberdeen : Institute of Electrical and Electronics Engineers (IEEE), 2017, 1-6

ISBN
978-1-5090-5278-3

Skup
IEEE OCEANS 2017 - Aberdeen

Mjesto i datum
Aberdeen, Ujedinjeno Kraljevstvo, 19.06.2017. - 22.06.2017

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
Sensors ; Three-dimensional displays ; Cameras ; Sonar ; Pose estimation ; Acoustics ; Real-time systems

Sažetak
Within the EU FP7 project “Cognitive autonomous diving buddy (CADDY)”, work has been made to assist and monitor divers through Autonomous Underwater Vehicles (AUVs) during their long underwater expeditions. To achieve this goal, one milestone is to give the AUV the capability to track the diver's whereabouts at all times. Inertial sensors are mounted on the diver's body to transmit acoustically his relative position and orientation to the AUV using an Ultra-Short Baseline (USBL) system. However, the acoustic system is prone to give erroneous data depending on the surrounding geography, modems alignment and sensors calibration, plus its low transmission rate does not allow real time performance. To overcome these drawbacks and complement the acoustic set-up, this paper presents a general framework to detect and estimate the diver's body pose based on generated point clouds from a stereo camera. Our method is based on a Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) that captures the temporal relations between global point cloud descriptors as they change with the diver movements. Since the analysis is made on time sequences rather than on one-shot visual information, the framework is robust against the distortions and poor quality typical in underwater while still providing real time performance. We focus on the description of the image-processing and LSTM-RNN pipeline, as well on its validation with a dataset created during several field trial experiments.

Izvorni jezik
Engleski

Znanstvena područja
Elektrotehnika



POVEZANOST RADA

Projekti:
EK-FP7-611373 - Kognitivni autonomni ronilački prijatelj (CADDY) (Mišković, Nikola, EK ) ( CroRIS)

Ustanove:
Fakultet elektrotehnike i računarstva, Zagreb

Profili:

Avatar Url Anja Babić (autor)

Avatar Url Nikola Mišković (autor)

Poveznice na cjeloviti tekst rada:

doi ieeexplore.ieee.org

Citiraj ovu publikaciju:

Chavez, Arturo Gomez; Mueller, Christian A.; Birk, Andreas; Babic, Anja; Miskovic, Nikola
Stereo-vision based diver pose estimation using LSTM recurrent neural networks for AUV navigation guidance // OCEANS 2017 Aberdeen Online Proceedings
Aberdeen: Institute of Electrical and Electronics Engineers (IEEE), 2017. str. 1-6 doi:10.1109/oceanse.2017.8085020 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
Chavez, A., Mueller, C., Birk, A., Babic, A. & Miskovic, N. (2017) Stereo-vision based diver pose estimation using LSTM recurrent neural networks for AUV navigation guidance. U: OCEANS 2017 Aberdeen Online Proceedings doi:10.1109/oceanse.2017.8085020.
@article{article, author = {Chavez, Arturo Gomez and Mueller, Christian A. and Birk, Andreas and Babic, Anja and Miskovic, Nikola}, year = {2017}, pages = {1-6}, DOI = {10.1109/oceanse.2017.8085020}, keywords = {Sensors, Three-dimensional displays, Cameras, Sonar, Pose estimation, Acoustics, Real-time systems}, doi = {10.1109/oceanse.2017.8085020}, isbn = {978-1-5090-5278-3}, title = {Stereo-vision based diver pose estimation using LSTM recurrent neural networks for AUV navigation guidance}, keyword = {Sensors, Three-dimensional displays, Cameras, Sonar, Pose estimation, Acoustics, Real-time systems}, publisher = {Institute of Electrical and Electronics Engineers (IEEE)}, publisherplace = {Aberdeen, Ujedinjeno Kraljevstvo} }
@article{article, author = {Chavez, Arturo Gomez and Mueller, Christian A. and Birk, Andreas and Babic, Anja and Miskovic, Nikola}, year = {2017}, pages = {1-6}, DOI = {10.1109/oceanse.2017.8085020}, keywords = {Sensors, Three-dimensional displays, Cameras, Sonar, Pose estimation, Acoustics, Real-time systems}, doi = {10.1109/oceanse.2017.8085020}, isbn = {978-1-5090-5278-3}, title = {Stereo-vision based diver pose estimation using LSTM recurrent neural networks for AUV navigation guidance}, keyword = {Sensors, Three-dimensional displays, Cameras, Sonar, Pose estimation, Acoustics, Real-time systems}, publisher = {Institute of Electrical and Electronics Engineers (IEEE)}, publisherplace = {Aberdeen, Ujedinjeno Kraljevstvo} }

Časopis indeksira:


  • Web of Science Core Collection (WoSCC)
    • Science Citation Index Expanded (SCI-EXP)
    • Conference Proceedings Citation Index - Science (CPCI-S)
  • Scopus

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