7,122 research outputs found
Gauge invariance induced relations and the equivalence between distinct approaches to NLSM amplitudes
In this paper, we derive generalized Bern-Carrasco-Johansson relations for
color-ordered Yang-Mills amplitudes by imposing gauge invariance conditions and
dimensional reduction appropriately on the new discovered graphic expansion of
Einstein-Yang-Mills amplitudes. These relations are also satisfied by
color-ordered amplitudes in other theories such as color-scalar theory,
bi-scalar theory and nonlinear sigma model (NLSM). As an application of the
gauge invariance induced relations, we further prove that the three types of
BCJ numerators in NLSM , which are derived from Feynman rules, Abelian Z-theory
and Cachazo-He- Yuan formula respectively, produce the same total amplitudes.
In other words, the three distinct approaches to NLSM amplitudes are equivalent
to each other.Comment: 40pages, 2 figure
DroTrack: High-speed Drone-based Object Tracking Under Uncertainty
We present DroTrack, a high-speed visual single-object tracking framework for
drone-captured video sequences. Most of the existing object tracking methods
are designed to tackle well-known challenges, such as occlusion and cluttered
backgrounds. The complex motion of drones, i.e., multiple degrees of freedom in
three-dimensional space, causes high uncertainty. The uncertainty problem leads
to inaccurate location predictions and fuzziness in scale estimations. DroTrack
solves such issues by discovering the dependency between object representation
and motion geometry. We implement an effective object segmentation based on
Fuzzy C Means (FCM). We incorporate the spatial information into the membership
function to cluster the most discriminative segments. We then enhance the
object segmentation by using a pre-trained Convolution Neural Network (CNN)
model. DroTrack also leverages the geometrical angular motion to estimate a
reliable object scale. We discuss the experimental results and performance
evaluation using two datasets of 51,462 drone-captured frames. The combination
of the FCM segmentation and the angular scaling increased DroTrack precision by
up to and decreased the centre location error by pixels on average.
DroTrack outperforms all the high-speed trackers and achieves comparable
results in comparison to deep learning trackers. DroTrack offers high frame
rates up to 1000 frame per second (fps) with the best location precision, more
than a set of state-of-the-art real-time trackers.Comment: 10 pages, 12 figures, FUZZ-IEEE 202
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