34,439 research outputs found
Going Deeper into Action Recognition: A Survey
Understanding human actions in visual data is tied to advances in
complementary research areas including object recognition, human dynamics,
domain adaptation and semantic segmentation. Over the last decade, human action
analysis evolved from earlier schemes that are often limited to controlled
environments to nowadays advanced solutions that can learn from millions of
videos and apply to almost all daily activities. Given the broad range of
applications from video surveillance to human-computer interaction, scientific
milestones in action recognition are achieved more rapidly, eventually leading
to the demise of what used to be good in a short time. This motivated us to
provide a comprehensive review of the notable steps taken towards recognizing
human actions. To this end, we start our discussion with the pioneering methods
that use handcrafted representations, and then, navigate into the realm of deep
learning based approaches. We aim to remain objective throughout this survey,
touching upon encouraging improvements as well as inevitable fallbacks, in the
hope of raising fresh questions and motivating new research directions for the
reader
Imitating Driver Behavior with Generative Adversarial Networks
The ability to accurately predict and simulate human driving behavior is
critical for the development of intelligent transportation systems. Traditional
modeling methods have employed simple parametric models and behavioral cloning.
This paper adopts a method for overcoming the problem of cascading errors
inherent in prior approaches, resulting in realistic behavior that is robust to
trajectory perturbations. We extend Generative Adversarial Imitation Learning
to the training of recurrent policies, and we demonstrate that our model
outperforms rule-based controllers and maximum likelihood models in realistic
highway simulations. Our model both reproduces emergent behavior of human
drivers, such as lane change rate, while maintaining realistic control over
long time horizons.Comment: 8 pages, 6 figure
Double-slit and electromagnetic models to complete quantum mechanics
We analyze a realistic microscopic model for electronic scattering with the
neutral differential delay equations of motion of point charges of the
Wheeler-Feynman electrodynamics. We propose a microscopic model according to
the electrodynamics of point charges, complex enough to describe the essential
physics. Our microscopic model reaches a simple qualitative agreement with the
experimental results as regards interference in double-slit scattering and in
electronic scattering by crystals. We discuss our model in the light of
existing experimental results, including a qualitative disagreement found for
the double-slit experiment. We discuss an approximation for the complex neutral
differential delay equations of our model using piecewise-defined
(discontinuous) velocities for all charges and piecewise-constant-velocities
for the scattered charge. Our approximation predicts the De Broglie wavelength
as an inverse function of the incoming velocity and in the correct order of
magnitude. We explain the scattering by crystals in the light of the same
simplified modeling with Einstein-local interactions. We include a discussion
of the qualitative properties of the neutral-delay-equations of electrodynamics
to stimulate future experimental tests on the possibility to complete quantum
mechanics with electromagnetic models.Comment: 4 figures, the same post-publication typos over the published version
of Journal of Computational and Theoretical Nanoscience, only that these
correction are not marked in red as in V7, this one is for a recollectio
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