871 research outputs found
The Diver with a Rotor
We present and analyse a simple model for the twisting somersault. The model
is a rigid body with a rotor attached which can be switched on and off. This
makes it simple enough to devise explicit analytical formulas whilst still
maintaining sufficient complexity to preserve the shape-changing dynamics
essential for twisting somersaults in springboard and platform diving. With
`rotor on' and with `rotor off' the corresponding Euler-type equations can be
solved, and the essential quantities characterising the dynamics, such as the
periods and rotation numbers, can be computed in terms of complete elliptic
integrals. Thus we arrive at explicit formulas for how to achieve a dive with m
somersaults and n twists in a given total time. This can be thought of as a
special case of a geometric phase formula due to Cabrera 2007.Comment: 15 pages, 6 figure
WiSARD: A Labeled Visual and Thermal Image Dataset for Wilderness Search and Rescue
Sensor-equipped unoccupied aerial vehicles (UAVs) have the potential to help
reduce search times and alleviate safety risks for first responders carrying
out Wilderness Search and Rescue (WiSAR) operations, the process of finding and
rescuing person(s) lost in wilderness areas. Unfortunately, visual sensors
alone do not address the need for robustness across all the possible terrains,
weather, and lighting conditions that WiSAR operations can be conducted in. The
use of multi-modal sensors, specifically visual-thermal cameras, is critical in
enabling WiSAR UAVs to perform in diverse operating conditions. However, due to
the unique challenges posed by the wilderness context, existing dataset
benchmarks are inadequate for developing vision-based algorithms for autonomous
WiSAR UAVs. To this end, we present WiSARD, a dataset with roughly 56,000
labeled visual and thermal images collected from UAV flights in various
terrains, seasons, weather, and lighting conditions. To the best of our
knowledge, WiSARD is the first large-scale dataset collected with multi-modal
sensors for autonomous WiSAR operations. We envision that our dataset will
provide researchers with a diverse and challenging benchmark that can test the
robustness of their algorithms when applied to real-world (life-saving)
applications
MISFIT-V: Misaligned Image Synthesis and Fusion using Information from Thermal and Visual
Detecting humans from airborne visual and thermal imagery is a fundamental
challenge for Wilderness Search-and-Rescue (WiSAR) teams, who must perform this
function accurately in the face of immense pressure. The ability to fuse these
two sensor modalities can potentially reduce the cognitive load on human
operators and/or improve the effectiveness of computer vision object detection
models. However, the fusion task is particularly challenging in the context of
WiSAR due to hardware limitations and extreme environmental factors. This work
presents Misaligned Image Synthesis and Fusion using Information from Thermal
and Visual (MISFIT-V), a novel two-pronged unsupervised deep learning approach
that utilizes a Generative Adversarial Network (GAN) and a cross-attention
mechanism to capture the most relevant features from each modality.
Experimental results show MISFIT-V offers enhanced robustness against
misalignment and poor lighting/thermal environmental conditions compared to
existing visual-thermal image fusion methods
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