2,730 research outputs found
Some NASA contributions to human factors engineering: A survey
This survey presents the NASA contributions to the state of the art of human factors engineering, and indicates that these contributions have a variety of applications to nonaerospace activities. Emphasis is placed on contributions relative to man's sensory, motor, decisionmaking, and cognitive behavior and on applications that advance human factors technology
Engineering data compendium. Human perception and performance. User's guide
The concept underlying the Engineering Data Compendium was the product of a research and development program (Integrated Perceptual Information for Designers project) aimed at facilitating the application of basic research findings in human performance to the design and military crew systems. The principal objective was to develop a workable strategy for: (1) identifying and distilling information of potential value to system design from the existing research literature, and (2) presenting this technical information in a way that would aid its accessibility, interpretability, and applicability by systems designers. The present four volumes of the Engineering Data Compendium represent the first implementation of this strategy. This is the first volume, the User's Guide, containing a description of the program and instructions for its use
Underwater Applications of Acoustical Holography
The paper describes the basic technique of acoustical holography. Requirements for recording the acoustical hologram are discussed with its ability for underwater imaging in view. Some practical systems for short-range and medium-range imaging are described. The advantages of acoustical holography over optical imaging, acoustical imaging and sonars are outlined
TVL<sub>1</sub> Planarity Regularization for 3D Shape Approximation
The modern emergence of automation in many industries has given impetus to extensive research into mobile robotics. Novel perception technologies now enable cars to drive autonomously, tractors to till a field automatically and underwater robots to construct pipelines. An essential requirement to facilitate both perception and autonomous navigation is the analysis of the 3D environment using sensors like laser scanners or stereo cameras. 3D sensors generate a very large number of 3D data points when sampling object shapes within an environment, but crucially do not provide any intrinsic information about the environment which the robots operate within.
This work focuses on the fundamental task of 3D shape reconstruction and modelling from 3D point clouds. The novelty lies in the representation of surfaces by algebraic functions having limited support, which enables the extraction of smooth consistent implicit shapes from noisy samples with a heterogeneous density. The minimization of total variation of second differential degree makes it possible to enforce planar surfaces which often occur in man-made environments. Applying the new technique means that less accurate, low-cost 3D sensors can be employed without sacrificing the 3D shape reconstruction accuracy
Oceanus.
v. 28, no. 1 (1985
Activity-conditioned continuous human pose estimation for performance analysis of athletes using the example of swimming
In this paper we consider the problem of human pose estimation in real-world
videos of swimmers. Swimming channels allow filming swimmers simultaneously
above and below the water surface with a single stationary camera. These
recordings can be used to quantitatively assess the athletes' performance. The
quantitative evaluation, so far, requires manual annotations of body parts in
each video frame. We therefore apply the concept of CNNs in order to
automatically infer the required pose information. Starting with an
off-the-shelf architecture, we develop extensions to leverage activity
information - in our case the swimming style of an athlete - and the continuous
nature of the video recordings. Our main contributions are threefold: (a) We
apply and evaluate a fine-tuned Convolutional Pose Machine architecture as a
baseline in our very challenging aquatic environment and discuss its error
modes, (b) we propose an extension to input swimming style information into the
fully convolutional architecture and (c) modify the architecture for continuous
pose estimation in videos. With these additions we achieve reliable pose
estimates with up to +16% more correct body joint detections compared to the
baseline architecture.Comment: 10 pages, 9 figures, accepted at WACV 201
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