3,251 research outputs found
FlightGoggles: A Modular Framework for Photorealistic Camera, Exteroceptive Sensor, and Dynamics Simulation
FlightGoggles is a photorealistic sensor simulator for perception-driven
robotic vehicles. The key contributions of FlightGoggles are twofold. First,
FlightGoggles provides photorealistic exteroceptive sensor simulation using
graphics assets generated with photogrammetry. Second, it provides the ability
to combine (i) synthetic exteroceptive measurements generated in silico in real
time and (ii) vehicle dynamics and proprioceptive measurements generated in
motio by vehicle(s) in a motion-capture facility. FlightGoggles is capable of
simulating a virtual-reality environment around autonomous vehicle(s). While a
vehicle is in flight in the FlightGoggles virtual reality environment,
exteroceptive sensors are rendered synthetically in real time while all complex
extrinsic dynamics are generated organically through the natural interactions
of the vehicle. The FlightGoggles framework allows for researchers to
accelerate development by circumventing the need to estimate complex and
hard-to-model interactions such as aerodynamics, motor mechanics, battery
electrochemistry, and behavior of other agents. The ability to perform
vehicle-in-the-loop experiments with photorealistic exteroceptive sensor
simulation facilitates novel research directions involving, e.g., fast and
agile autonomous flight in obstacle-rich environments, safe human interaction,
and flexible sensor selection. FlightGoggles has been utilized as the main test
for selecting nine teams that will advance in the AlphaPilot autonomous drone
racing challenge. We survey approaches and results from the top AlphaPilot
teams, which may be of independent interest.Comment: Initial version appeared at IROS 2019. Supplementary material can be
found at https://flightgoggles.mit.edu. Revision includes description of new
FlightGoggles features, such as a photogrammetric model of the MIT Stata
Center, new rendering settings, and a Python AP
Simple Online and Realtime Tracking with a Deep Association Metric
Simple Online and Realtime Tracking (SORT) is a pragmatic approach to
multiple object tracking with a focus on simple, effective algorithms. In this
paper, we integrate appearance information to improve the performance of SORT.
Due to this extension we are able to track objects through longer periods of
occlusions, effectively reducing the number of identity switches. In spirit of
the original framework we place much of the computational complexity into an
offline pre-training stage where we learn a deep association metric on a
large-scale person re-identification dataset. During online application, we
establish measurement-to-track associations using nearest neighbor queries in
visual appearance space. Experimental evaluation shows that our extensions
reduce the number of identity switches by 45%, achieving overall competitive
performance at high frame rates.Comment: 5 pages, 1 figur
Towards a Smart Selection of Hybrid Platforms for Multimedia Processing
Proceedings of the First PhD Symposium on Sustainable Ultrascale
Computing Systems (NESUS PhD 2016) Timisoara, Romania. February 8-11, 2016.Nowadays, images and videos have been present everywhere, they can come directly from camera, mobile devices
or from other peoples that share their images and videos. The latter are used to illustrate different objects in a
large number of situations. This makes from image and video processing algorithms a very important tool used for
various domains related to computer vision such as video surveillance, medical imaging and database (images and
videos) indexation methods. The performance of these algorithms have been so reduced due the the high intensive
computation required when using new image and video standards. In this paper, we propose a new framework that
allows users to select in a smart and efficient way the processing units (GPU or/and CPU) within heterogeneous
systems, when treating different kinds of multimedia objects : single image, multiple images, multiple videos and
video in real time. The framework disposes of different image and video primitive functions that are implemented
on GPU, such as shape (silhouette) detection, motion tracking using optical flow estimation, edges and corners
detection. We have exploited these functions for several situations such as indexing videos, segmenting vertebrae
in in X-ray and MR images, detecting and localizing event in multi-user scenarios. Experimentation showed
interesting accelerations ranging from 6 to 118, by comparison with sequential implementations. Moreover, the
parallel and heterogeneous implementations offered lower power consumption as a result for the fast treatment.European Cooperation in Science and Technology. COS
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