3,338 research outputs found
Road User Detection in Videos
Successive frames of a video are highly redundant, and the most popular
object detection methods do not take advantage of this fact. Using multiple
consecutive frames can improve detection of small objects or difficult examples
and can improve speed and detection consistency in a video sequence, for
instance by interpolating features between frames. In this work, a novel
approach is introduced to perform online video object detection using two
consecutive frames of video sequences involving road users. Two new models,
RetinaNet-Double and RetinaNet-Flow, are proposed, based respectively on the
concatenation of a target frame with a preceding frame, and the concatenation
of the optical flow with the target frame. The models are trained and evaluated
on three public datasets. Experiments show that using a preceding frame
improves performance over single frame detectors, but using explicit optical
flow usually does not
Road User Detection in Videos
Successive frames of a video are highly redundant, and the most popular
object detection methods do not take advantage of this fact. Using multiple
consecutive frames can improve detection of small objects or difficult examples
and can improve speed and detection consistency in a video sequence, for
instance by interpolating features between frames. In this work, a novel
approach is introduced to perform online video object detection using two
consecutive frames of video sequences involving road users. Two new models,
RetinaNet-Double and RetinaNet-Flow, are proposed, based respectively on the
concatenation of a target frame with a preceding frame, and the concatenation
of the optical flow with the target frame. The models are trained and evaluated
on three public datasets. Experiments show that using a preceding frame
improves performance over single frame detectors, but using explicit optical
flow usually does not
RN-VID: A Feature Fusion Architecture for Video Object Detection
Consecutive frames in a video are highly redundant. Therefore, to perform the
task of video object detection, executing single frame detectors on every frame
without reusing any information is quite wasteful. It is with this idea in mind
that we propose RN-VID (standing for RetinaNet-VIDeo), a novel approach to
video object detection. Our contributions are twofold. First, we propose a new
architecture that allows the usage of information from nearby frames to enhance
feature maps. Second, we propose a novel module to merge feature maps of same
dimensions using re-ordering of channels and 1 x 1 convolutions. We then
demonstrate that RN-VID achieves better mean average precision (mAP) than
corresponding single frame detectors with little additional cost during
inference
Stringent Limits on the Polarized Submillimeter Emission from Protoplanetary Disks
We present arcsecond-resolution Submillimeter Array (SMA) polarimetric
observations of the 880 um continuum emission from the protoplanetary disks
around two nearby stars, HD 163296 and TW Hydrae. Although previous
observations and theoretical work have suggested that a 2-3% polarization
fraction should be common for the millimeter continuum emission from such
disks, we detect no polarized continuum emission above a 3-sigma upper limit of
7 mJy in each arcsecond-scale beam, or <1% in integrated continuum emission. We
compare the SMA upper limits with the predictions from the exploratory Cho &
Lazarian (2007) model of polarized emission from T Tauri disks threaded by
toroidal magnetic fields, and rule out their fiducial model at the ~10-sigma
level. We explore some potential causes for this discrepancy, focusing on model
parameters that describe the shape, magnetic field alignment, and size
distribution of grains in the disk. We also investigate related effects like
the magnetic field strength and geometry, scattering off of large grains, and
the efficiency of grain alignment, including recent advances in grain alignment
theory, which are not considered in the fiducial model. We discuss the impact
each parameter would have on the data and determine that the suppression of
polarized emission plausibly arises from rounding of large grains, reduced
efficiency of grain alignment with the magnetic field, and/or some degree of
magnetic field tangling (perhaps due to turbulence). A poloidal magnetic field
geometry could also reduce the polarization signal, particularly for a face-on
viewing geometry like the TW Hya disk. The data provided here offer the most
stringent limits to date on the polarized millimeter-wavelength emission from
disks around young stars.Comment: 15 pages, 6 figures, accepted for publication in Ap
Using BEAM Software to Simulate the Introduction of On-Demand, Automated, and Electric Shuttles for Last Mile Connectivity in Santa Clara County
Despite growing interest in low-speed automated shuttles, pilot deployments have only just begun in a few places in the U.S., and there is a lack of studies that estimate the impacts of a widespread deployment of automated shuttles designed to supplement existing transit networks. This project estimated the potential impacts of automated shuttles based on a deployment scenario generated for a sample geographic area: Santa Clara County, California. The project identified sample deployment markets within Santa Clara County using a GIS screening exercise; tested the mode share changes of an automated shuttle deployment scenario using BEAM, an open-source beta software developed at the Lawrence Berkeley National Laboratory to run traffic simulations with MATSim; elaborated the model outputs within the R environment; and then estimated the related impacts. The main findings have been that the BEAM software, despite still being in its beta version, was able to model a scenario with the automated shuttle service: this report illustrates the potential of the software and the lessons learned. Regarding transportation aspects, the model estimated automated shuttle use throughout the county, with a higher rate of use in the downtown San José area. The shuttles would be preferred mainly by people who had been using gasoline-powered ride hail vehicles for A-to-B trips or going to the bus stop, as well as walking trips and a few car trips directed to public transport stops. As a result, the shuttles contributed to a small decrease in emissions of air pollutants, provided a competitive solution for short trips, and increased the overall use of the public transport system. The shuttles also presented a solution for short night trips—mainly between midnight and 2 am—when there are not many options for moving between points A and B. The conclusion is that the automated shuttle service is a good solution in certain contexts and can increase public transit ridership overall
Observation of the topological Anderson insulator in disordered atomic wires
Topology and disorder have deep connections and a rich combined influence on
quantum transport. In order to probe these connections, we synthesized
one-dimensional chiral symmetric wires with controllable disorder via
spectroscopic Hamiltonian engineering, based on the laser-driven coupling of
discrete momentum states of ultracold atoms. We characterize the system's
topology through measurement of the mean chiral displacement of the bulk
density extracted from quench dynamics. We find evidence for the topological
Anderson insulator phase, in which the band structure of an otherwise trivial
wire is driven topological by the presence of added disorder. In addition, we
observed the robustness of topological wires to weak disorder and measured the
transition to a trivial phase in the presence of strong disorder. Atomic
interactions in this quantum simulation platform will enable future
realizations of strongly interacting topological fluids.Comment: 6 pages, 3 figures; 9 pages of supplementary material
Renormalization of composite operators
The blocked composite operators are defined in the one-component Euclidean
scalar field theory, and shown to generate a linear transformation of the
operators, the operator mixing. This transformation allows us to introduce the
parallel transport of the operators along the RG trajectory. The connection on
this one-dimensional manifold governs the scale evolution of the operator
mixing. It is shown that the solution of the eigenvalue problem of the
connection gives the various scaling regimes and the relevant operators there.
The relation to perturbative renormalization is also discussed in the framework
of the theory in dimension .Comment: 24 pages, revtex (accepted by Phys. Rev. D), changes in introduction
and summar
A supervised clustering approach for fMRI-based inference of brain states
We propose a method that combines signals from many brain regions observed in
functional Magnetic Resonance Imaging (fMRI) to predict the subject's behavior
during a scanning session. Such predictions suffer from the huge number of
brain regions sampled on the voxel grid of standard fMRI data sets: the curse
of dimensionality. Dimensionality reduction is thus needed, but it is often
performed using a univariate feature selection procedure, that handles neither
the spatial structure of the images, nor the multivariate nature of the signal.
By introducing a hierarchical clustering of the brain volume that incorporates
connectivity constraints, we reduce the span of the possible spatial
configurations to a single tree of nested regions tailored to the signal. We
then prune the tree in a supervised setting, hence the name supervised
clustering, in order to extract a parcellation (division of the volume) such
that parcel-based signal averages best predict the target information.
Dimensionality reduction is thus achieved by feature agglomeration, and the
constructed features now provide a multi-scale representation of the signal.
Comparisons with reference methods on both simulated and real data show that
our approach yields higher prediction accuracy than standard voxel-based
approaches. Moreover, the method infers an explicit weighting of the regions
involved in the regression or classification task
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