66,628 research outputs found
Action Recognition in Videos: from Motion Capture Labs to the Web
This paper presents a survey of human action recognition approaches based on
visual data recorded from a single video camera. We propose an organizing
framework which puts in evidence the evolution of the area, with techniques
moving from heavily constrained motion capture scenarios towards more
challenging, realistic, "in the wild" videos. The proposed organization is
based on the representation used as input for the recognition task, emphasizing
the hypothesis assumed and thus, the constraints imposed on the type of video
that each technique is able to address. Expliciting the hypothesis and
constraints makes the framework particularly useful to select a method, given
an application. Another advantage of the proposed organization is that it
allows categorizing newest approaches seamlessly with traditional ones, while
providing an insightful perspective of the evolution of the action recognition
task up to now. That perspective is the basis for the discussion in the end of
the paper, where we also present the main open issues in the area.Comment: Preprint submitted to CVIU, survey paper, 46 pages, 2 figures, 4
table
ASIME 2018 White Paper. In-Space Utilisation of Asteroids: Asteroid Composition -- Answers to Questions from the Asteroid Miners
In keeping with the Luxembourg government's initiative to support the future
use of space resources, ASIME 2018 was held in Belval, Luxembourg on April
16-17, 2018.
The goal of ASIME 2018: Asteroid Intersections with Mine Engineering, was to
focus on asteroid composition for advancing the asteroid in-space resource
utilisation domain. What do we know about asteroid composition from
remote-sensing observations? What are the potential caveats in the
interpretation of Earth-based spectral observations? What are the next steps to
improve our knowledge on asteroid composition by means of ground-based and
space-based observations and asteroid rendez-vous and sample return missions?
How can asteroid mining companies use this knowledge?
ASIME 2018 was a two-day workshop of almost 70 scientists and engineers in
the context of the engineering needs of space missions with in-space asteroid
utilisation. The 21 Questions from the asteroid mining companies were sorted
into the four asteroid science themes: 1) Potential Targets, 2)
Asteroid-Meteorite Links, 3) In-Situ Measurements and 4) Laboratory
Measurements. The Answers to those Questions were provided by the scientists
with their conference presentations and collected by A. Graps or edited
directly into an open-access collaborative Google document or inserted by A.
Graps using additional reference materials. During the ASIME 2018, first day
and second day Wrap-Ups, the answers to the questions were discussed further.
New readers to the asteroid mining topic may find the Conversation boxes and
the Mission Design discussions especially interesting.Comment: Outcome from the ASIME 2018: Asteroid Intersections with Mine
Engineering, Luxembourg. April 16-17, 2018. 65 Pages. arXiv admin note:
substantial text overlap with arXiv:1612.0070
NTU RGB+D 120: A Large-Scale Benchmark for 3D Human Activity Understanding
Research on depth-based human activity analysis achieved outstanding
performance and demonstrated the effectiveness of 3D representation for action
recognition. The existing depth-based and RGB+D-based action recognition
benchmarks have a number of limitations, including the lack of large-scale
training samples, realistic number of distinct class categories, diversity in
camera views, varied environmental conditions, and variety of human subjects.
In this work, we introduce a large-scale dataset for RGB+D human action
recognition, which is collected from 106 distinct subjects and contains more
than 114 thousand video samples and 8 million frames. This dataset contains 120
different action classes including daily, mutual, and health-related
activities. We evaluate the performance of a series of existing 3D activity
analysis methods on this dataset, and show the advantage of applying deep
learning methods for 3D-based human action recognition. Furthermore, we
investigate a novel one-shot 3D activity recognition problem on our dataset,
and a simple yet effective Action-Part Semantic Relevance-aware (APSR)
framework is proposed for this task, which yields promising results for
recognition of the novel action classes. We believe the introduction of this
large-scale dataset will enable the community to apply, adapt, and develop
various data-hungry learning techniques for depth-based and RGB+D-based human
activity understanding. [The dataset is available at:
http://rose1.ntu.edu.sg/Datasets/actionRecognition.asp]Comment: IEEE Transactions on Pattern Analysis and Machine Intelligence
(TPAMI
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