The Experts below are selected from a list of 258 Experts worldwide ranked by ideXlab platform
Nasa - One of the best experts on this subject based on the ideXlab platform.
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STS-33 EVA Prep and Post with Gregory, Blaha, Carter, Thorton, and Musgrave in FFT
2019Co-Authors: NasaAbstract: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.
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STS-111 Crew Training Clip
2017Co-Authors: NasaAbstract: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.
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Expedition 3 Crew Training Clips
2017Co-Authors: NasaAbstract: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.
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STS-112 Mission Highlights Resource, Part 3 of 3
2017Co-Authors: NasaAbstract:The STS-112 Mission begins with a view of the center radiator on the S(1) Truss. A good view of the International Space Station's (ISS) Destiny Laboratory, Soyuz Crew Return Vehicle and Quest Airlock are shown from a video camera located at the end of the S(1) Truss Segment. The ISS Canadarm 2 is shown getting in position for spacewalk three. Highlights of flight day eight begin with Pilot Pam Melroy and Mission Specialist Fyodur Yurchikhin shown inside of the Quest Airlock closing the hatch as spacewalkers David Wolf and Piers Sellers move in the outer compartment of the Airlock to begin Extravehicular Activity 3 (EVA 3). During EVA 3, Dave Wolf and Piers Sellers are installing spool positioning devices on ammonia lines located on the ISS. Robot Arm Operators Peggy Whitson and Sandy Magnus are shown reviewing procedures for operating the robot arm. A view of Piers Seller climbing back into the Quest Airlock is presented. During flight day nine, robot arm operators Pam Melroy, Jeff Ashby and Peggy Whitson are in the process of removing spacesuits worn by David Wolf and Piers Sellers. A final farewell of the nine crewmembers shown inside of the Destiny Laboratory is presented during flight day ten. The undocking of Space Shuttle Atlantis from the International Space Station is shown on flight day eleven. This presentation ends on flight day 12 with a view of head up displays and the actual landing of the Space Shuttle Atlantis.
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STS-37 Gamma Ray Observatory Arrival and VPF Activities
2017Co-Authors: NasaAbstract:Live footage shows the STS-37 Gamma Ray Observatory, its move to the airlock, the removal of its plastic covering, and its lift to the work-stand.
Jeanmarc Odobez - One of the best experts on this subject based on the ideXlab platform.
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unicity a depth maps database for people detection in security Airlocks
Advanced Video and Signal Based Surveillance, 2018Co-Authors: Joel Dumoulin, Olivier Canevet, Michael Villamizar, Hugo Nunes, Omar Abou Khaled, Elena Mugellini, Fabrice Moscheni, Jeanmarc OdobezAbstract: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.
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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), 2018Co-Authors: Joel Dumoulin, Olivier Canevet, Michael Villamizar, Hugo Nunes, Omar Abou Khaled, Elena Mugellini, Fabrice Moscheni, Jeanmarc OdobezAbstract: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 - One of the best experts on this subject based on the ideXlab platform.
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unicity a depth maps database for people detection in security Airlocks
Advanced Video and Signal Based Surveillance, 2018Co-Authors: Joel Dumoulin, Olivier Canevet, Michael Villamizar, Hugo Nunes, Omar Abou Khaled, Elena Mugellini, Fabrice Moscheni, Jeanmarc OdobezAbstract: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.
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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), 2018Co-Authors: Joel Dumoulin, Olivier Canevet, Michael Villamizar, Hugo Nunes, Omar Abou Khaled, Elena Mugellini, Fabrice Moscheni, Jeanmarc OdobezAbstract: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.
John Springhetti - One of the best experts on this subject based on the ideXlab platform.
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JSC - Twenty Foot Vacuum Chamber
2020Co-Authors: John SpringhettiAbstract:The 20 ft is a vacuum chamber with an airlock and a rapid decompression chamber. The volume is divided into three levels by non-pressure-bearing floors, which provide atmospheric isolation.
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JSC - Space Station Airlock Vacuum Chamber
2020Co-Authors: John SpringhettiAbstract:The Space Station Airlock Test Article (SSATA) supports the International Space Station (ISS) Program for Airlock and Extravehicular (EVA) hardware testing.
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JSC - Environmental Test Article Vacuum Chamber
2020Co-Authors: John SpringhettiAbstract:The Environmental Test Article (ETA) was developed to simulate the volume of a Shuttle Mid Deck, Flight Deck, and External Airlock.
Fabrice Moscheni - One of the best experts on this subject based on the ideXlab platform.
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unicity a depth maps database for people detection in security Airlocks
Advanced Video and Signal Based Surveillance, 2018Co-Authors: Joel Dumoulin, Olivier Canevet, Michael Villamizar, Hugo Nunes, Omar Abou Khaled, Elena Mugellini, Fabrice Moscheni, Jeanmarc OdobezAbstract: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.
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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), 2018Co-Authors: Joel Dumoulin, Olivier Canevet, Michael Villamizar, Hugo Nunes, Omar Abou Khaled, Elena Mugellini, Fabrice Moscheni, Jeanmarc OdobezAbstract: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.
