Aerospace & Defense Technology - October 2021 - 30

Tech Briefs
ities in a real environment. Recent advances
in 3D motion capture technology,
3D depth cameras using structured light
or time-of-flight sensors, and 3D information
recovery from 2D images/videos
have provided commercially viable approaches
and hardware platforms to capture
3D data in real-time and have been
nurturing a potential breakthrough solution
to such problems by using 3D data.
3D Motion Capture
A computational approach for action
prediction can extend their findings to
machines and also promote further research
in human prediction and intention
sensing. Apparently, a practical prediction
system must output a rapid
response for partial observations. This
brings up a new challenge to the computational
models and motivates machine
learning researchers to make more
3D Motion Reconstruction
Post Estimation and Actvity Uderstanding
progress. Moreover, action prediction will
need to model temporal structures and
may raise an important advance for action
recognition.
Quantitive Biomechanical Assessments
Interaction
3D Camera Sensing
Fusion
Interaction
3D Visual Modeling
Computational Motion Manifold
Kinetic Sensor
3D Motion Tracking
The 3D human data acquisition platform
used for this research consists of a
set of 3D motion capture sensors (e.g.
Vicon) and a set of 3D cameras (e.g.
Kinect) that are synchronized and integrated
to cross-validate data acquisition,
as shown in the accompanying figure. As
illustrated in the computing (right) module,
new methodologies of 3D motion reconstruction
and 3D visual modeling
will be developed to fill in the gap between
vision and motion data and form
the computational component to drive
interactions. The gap between the middle
level and low level data flow is filled
by parametric and composable low-dimensional
manifold representations.
Such integrated data acquisition and
methodologies will link the visual representations
to quantitative biomechanical
assessment of the human movements in
the form of immersive activities, which
aid the development of human models
and assist in the progressive parametric
refinement of modeling.
This work was done by Yun Fu of NortheastComputational
Visual Analysisi
3D human data acquisition platform.
ern University for the Army Research Office.
For more information, download the Technical
Support Package (free white paper) at
www.aerodefensetech.com/tsp under the
DAQ, Testing & Sensors category. ARL-0243
Coastal Lidar and Radar Imaging System (CLARIS) Lidar
Data Report
An analysis of shoreline change, dune volume, beach volume, beach slope, and cumulative elevation
change along the northern Outer Banks of North Carolina near the CHL Field Research Facility over
a 6-year study period.
Army Engineer Research and Development Center, Vicksburg, Mississippi
T
30
Intro
Cov
he dynamic nature of the nation's
coastlines necessitates frequent
shoreline monitoring and mapping.
The U.S. Army Engineer Research and
Development Center, Coastal and Hydraulics
Laboratory (CHL), Field Research
Facility (FRF), has collected
datasets on the nearshore zone's changing
conditions for over 40 years. During
the course of these efforts, CHL has continued
to develop different technologies
to refine shoreline monitoring techniques,
with a particular focus on the
application of remote sensing technology
to coastal monitoring. Light detection
and ranging (lidar) scanners have
proven useful for the CHL coastal measurement
efforts, providing highly dewww.aerodefensetech.com
ToC
+
-
tailed
data of coastal change and hydrodynamic
processes.
Lidar is frequently collected from stationary
ground-based platforms, which
provide fine detail (100s to 1000s of
points per meter) in one location or mobile
airborne sampling approaches that
provide coverage over large areas but at
lower resolution (1 to 10s of points per
Aerospace & Defense Technology, October 2021
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Aerospace & Defense Technology - October 2021

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