6,904 research outputs found
Immersive video conferencing architecture using game engine technology
This paper introduces the use of gaming technology for the creation of immersive video conferencing systems. The system integrates virtual meeting rooms with avatars and life video feeds, shared across different clients. Video analysis is used to create a sense of immersiveness by introducing aspects of the real world in the virtual environment. This architecture will ease and stimulate the development of immersive and intelligent telepresence systems
Requirement analysis and sensor specifications – First version
In this first version of the deliverable, we make the following contributions: to design the
WEKIT capturing platform and the associated experience capturing API, we use a
methodology for system engineering that is relevant for different domains such as: aviation,
space, and medical and different professions such as: technicians, astronauts, and medical
staff. Furthermore, in the methodology, we explore the system engineering process and how
it can be used in the project to support the different work packages and more importantly
the different deliverables that will follow the current.
Next, we provide a mapping of high level functions or tasks (associated with experience
transfer from expert to trainee) to low level functions such as: gaze, voice, video, body
posture, hand gestures, bio-signals, fatigue levels, and location of the user in the
environment. In addition, we link the low level functions to their associated sensors.
Moreover, we provide a brief overview of the state-of-the-art sensors in terms of their
technical specifications, possible limitations, standards, and platforms.
We outline a set of recommendations pertaining to the sensors that are most relevant for
the WEKIT project taking into consideration the environmental, technical and human
factors described in other deliverables. We recommend Microsoft Hololens (for Augmented
reality glasses), MyndBand and Neurosky chipset (for EEG), Microsoft Kinect and Lumo Lift
(for body posture tracking), and Leapmotion, Intel RealSense and Myo armband (for hand
gesture tracking). For eye tracking, an existing eye-tracking system can be customised to
complement the augmented reality glasses, and built-in microphone of the augmented
reality glasses can capture the expert’s voice. We propose a modular approach for the design
of the WEKIT experience capturing system, and recommend that the capturing system
should have sufficient storage or transmission capabilities.
Finally, we highlight common issues associated with the use of different sensors. We
consider that the set of recommendations can be useful for the design and integration of the
WEKIT capturing platform and the WEKIT experience capturing API to expedite the time
required to select the combination of sensors which will be used in the first prototype.WEKI
The Evolution of First Person Vision Methods: A Survey
The emergence of new wearable technologies such as action cameras and
smart-glasses has increased the interest of computer vision scientists in the
First Person perspective. Nowadays, this field is attracting attention and
investments of companies aiming to develop commercial devices with First Person
Vision recording capabilities. Due to this interest, an increasing demand of
methods to process these videos, possibly in real-time, is expected. Current
approaches present a particular combinations of different image features and
quantitative methods to accomplish specific objectives like object detection,
activity recognition, user machine interaction and so on. This paper summarizes
the evolution of the state of the art in First Person Vision video analysis
between 1997 and 2014, highlighting, among others, most commonly used features,
methods, challenges and opportunities within the field.Comment: First Person Vision, Egocentric Vision, Wearable Devices, Smart
Glasses, Computer Vision, Video Analytics, Human-machine Interactio
Vision-Based Production of Personalized Video
In this paper we present a novel vision-based system for the automated production of personalised video souvenirs for visitors in leisure and cultural heritage venues. Visitors are visually identified and tracked through a camera network. The system produces a personalized DVD souvenir at the end of a visitor’s stay allowing visitors to relive their experiences. We analyze how we identify visitors by fusing facial and body features, how we track visitors, how the tracker recovers from failures due to occlusions, as well as how we annotate and compile the final product. Our experiments demonstrate the feasibility of the proposed approach
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