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Airlocks

The Experts below are selected from a list of 258 Experts worldwide ranked by ideXlab platform

Nasa – 1st expert on this subject based on the ideXlab platform

  • STS-33 EVA Prep and Post with Gregory, Blaha, Carter, Thorton, and Musgrave in FFT
    , 2019
    Co-Authors: Nasa

    Abstract:

    This video shows the crew in the airlock of the FFT, talking with technicians about the extravehicular activity (EVA) equipment. Thornton and Carter put on EVA suits and enter the airlock as the other crew members help with checklists.

  • STS-111 Crew Training Clip
    , 2017
    Co-Authors: Nasa

    Abstract:

    The STS-111 Crew is in training for space flight. The crew consists of Commander Ken Cockrell, Pilot Paul Lockhart, Mission Specialists Franklin Chang-Diaz and Philippe Perrin. The crew training begins with Post Insertion Operations with the Full Fuselage Trainer (FFT). Franklin Chang-Diaz, Philippe Perrin and Paul Lockhart are shown in training for airlock and Neutral Buoyancy Lab (NBL) activities. Bailout in Crew Compartment Training (CCT) with Expedition Five is also shown. The crew also gets experience with photography, television, and habitation equipment.

  • Expedition 3 Crew Training Clips
    , 2017
    Co-Authors: Nasa

    Abstract:

    The Expedition 3 crewmembers, Frank Culbertson, Jr., Mikhail Turin, and Vladimir Dezhurov, are seen during various stages of their training. Footage includes Extravehicular Activity (EVA) Training at the Neutral Buoyancy Laboratory (NBL), EVA Preparation and Post Training in the International Space Station Airlock Mock-up, in the NBL Space Station Remote Manipulator System Workstation, and during the T-38 flight at Ellington Field.

Jeanmarc Odobez – 2nd expert on this subject based on the ideXlab platform

  • unicity a depth maps database for people detection in security Airlocks
    Advanced Video and Signal Based Surveillance, 2018
    Co-Authors: Joel Dumoulin, Olivier Canevet, Michael Villamizar, Hugo Nunes, Omar Abou Khaled, Elena Mugellini, Fabrice Moscheni, Jeanmarc Odobez

    Abstract:

    We introduce a new dataset, dubbed UNICITY1, for the task of detecting people in security Airlocks in top view depth images. If security companies have been relying on computer systems and algorithms for a long time, very few are trusting artificial intelligence and more specifically machine learning approaches in production environments. We are confident that the recent advances in these domains, especially with the democratization of deep learning, will open new horizons for security systems. We release this dataset to encourage the development of such approaches in the scientific community.UNICITY consists of 58k images collected from 65 recorded sequences with one or two people performing different behaviors including attacks and trickeries (e.g. tailgating2). It also provides full annotation of people such as the location of head and shoulders. As as result, UNICITY is perfectly suited for training and adapting machine learning algorithms for video surveillance applications. This paper presents the data collection, an evaluation protocol, as well as two baseline methods for attack detection.

  • AVSS – UNICITY: A depth maps database for people detection in security Airlocks
    2018 15th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), 2018
    Co-Authors: Joel Dumoulin, Olivier Canevet, Michael Villamizar, Hugo Nunes, Omar Abou Khaled, Elena Mugellini, Fabrice Moscheni, Jeanmarc Odobez

    Abstract:

    We introduce a new dataset, dubbed UNICITY1, for the task of detecting people in security Airlocks in top view depth images. If security companies have been relying on computer systems and algorithms for a long time, very few are trusting artificial intelligence and more specifically machine learning approaches in production environments. We are confident that the recent advances in these domains, especially with the democratization of deep learning, will open new horizons for security systems. We release this dataset to encourage the development of such approaches in the scientific community.UNICITY consists of 58k images collected from 65 recorded sequences with one or two people performing different behaviors including attacks and trickeries (e.g. tailgating2). It also provides full annotation of people such as the location of head and shoulders. As as result, UNICITY is perfectly suited for training and adapting machine learning algorithms for video surveillance applications. This paper presents the data collection, an evaluation protocol, as well as two baseline methods for attack detection.

Joel Dumoulin – 3rd expert on this subject based on the ideXlab platform

  • unicity a depth maps database for people detection in security Airlocks
    Advanced Video and Signal Based Surveillance, 2018
    Co-Authors: Joel Dumoulin, Olivier Canevet, Michael Villamizar, Hugo Nunes, Omar Abou Khaled, Elena Mugellini, Fabrice Moscheni, Jeanmarc Odobez

    Abstract:

    We introduce a new dataset, dubbed UNICITY1, for the task of detecting people in security Airlocks in top view depth images. If security companies have been relying on computer systems and algorithms for a long time, very few are trusting artificial intelligence and more specifically machine learning approaches in production environments. We are confident that the recent advances in these domains, especially with the democratization of deep learning, will open new horizons for security systems. We release this dataset to encourage the development of such approaches in the scientific community.UNICITY consists of 58k images collected from 65 recorded sequences with one or two people performing different behaviors including attacks and trickeries (e.g. tailgating2). It also provides full annotation of people such as the location of head and shoulders. As as result, UNICITY is perfectly suited for training and adapting machine learning algorithms for video surveillance applications. This paper presents the data collection, an evaluation protocol, as well as two baseline methods for attack detection.

  • AVSS – UNICITY: A depth maps database for people detection in security Airlocks
    2018 15th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), 2018
    Co-Authors: Joel Dumoulin, Olivier Canevet, Michael Villamizar, Hugo Nunes, Omar Abou Khaled, Elena Mugellini, Fabrice Moscheni, Jeanmarc Odobez

    Abstract:

    We introduce a new dataset, dubbed UNICITY1, for the task of detecting people in security Airlocks in top view depth images. If security companies have been relying on computer systems and algorithms for a long time, very few are trusting artificial intelligence and more specifically machine learning approaches in production environments. We are confident that the recent advances in these domains, especially with the democratization of deep learning, will open new horizons for security systems. We release this dataset to encourage the development of such approaches in the scientific community.UNICITY consists of 58k images collected from 65 recorded sequences with one or two people performing different behaviors including attacks and trickeries (e.g. tailgating2). It also provides full annotation of people such as the location of head and shoulders. As as result, UNICITY is perfectly suited for training and adapting machine learning algorithms for video surveillance applications. This paper presents the data collection, an evaluation protocol, as well as two baseline methods for attack detection.