186 research outputs found
EV-FlowNet: Self-Supervised Optical Flow Estimation for Event-based Cameras
Event-based cameras have shown great promise in a variety of situations where
frame based cameras suffer, such as high speed motions and high dynamic range
scenes. However, developing algorithms for event measurements requires a new
class of hand crafted algorithms. Deep learning has shown great success in
providing model free solutions to many problems in the vision community, but
existing networks have been developed with frame based images in mind, and
there does not exist the wealth of labeled data for events as there does for
images for supervised training. To these points, we present EV-FlowNet, a novel
self-supervised deep learning pipeline for optical flow estimation for event
based cameras. In particular, we introduce an image based representation of a
given event stream, which is fed into a self-supervised neural network as the
sole input. The corresponding grayscale images captured from the same camera at
the same time as the events are then used as a supervisory signal to provide a
loss function at training time, given the estimated flow from the network. We
show that the resulting network is able to accurately predict optical flow from
events only in a variety of different scenes, with performance competitive to
image based networks. This method not only allows for accurate estimation of
dense optical flow, but also provides a framework for the transfer of other
self-supervised methods to the event-based domain.Comment: 9 pages, 5 figures, 1 table. Accompanying video:
https://youtu.be/eMHZBSoq0sE. Dataset:
https://daniilidis-group.github.io/mvsec/, Robotics: Science and Systems 201
An Improved Bernstein-type Inequality for C-Mixing-type Processes and Its Application to Kernel Smoothing
There are many processes, particularly dynamic systems, that cannot be
described as strong mixing processes. \citet{maume2006exponential} introduced a
new mixing coefficient called C-mixing, which includes a large class of dynamic
systems. Based on this, \citet{hang2017bernstein} obtained a Bernstein-type
inequality for a geometric C-mixing process, which, modulo a logarithmic factor
and some constants, coincides with the standard result for the iid case. In
order to honor this pioneering work, we conduct follow-up research in this
paper and obtain an improved result under more general preconditions. We allow
for a weaker requirement for the semi-norm condition, fully non-stationarity,
non-isotropic sampling behavior. Our result covers the case in which the index
set of processes lies in for any given positive integer .
Here denotes the collection of all nonnegative integer-valued
-dimensional vector. This setting of index set takes both time and spatial
data into consideration. For our application, we investigate the theoretical
guarantee of multiple kernel-based nonparametric curve estimators for
C-Mixing-type processes. More specifically we firstly obtain the
-convergence rate of the kernel density estimator and then discuss
the attainability of optimality, which can also be regarded as an upate of the
result of \citet{hang2018kernel}. Furthermore, we investigate the uniform
convergence of the kernel-based estimators of the conditional mean and variance
function in a heteroscedastic nonparametric regression model. Under a mild
smoothing condition, these estimators are optimal. At last, we obtain the
uniform convergence rate of conditional mode function
Unsupervised Event-based Learning of Optical Flow, Depth, and Egomotion
In this work, we propose a novel framework for unsupervised learning for
event cameras that learns motion information from only the event stream. In
particular, we propose an input representation of the events in the form of a
discretized volume that maintains the temporal distribution of the events,
which we pass through a neural network to predict the motion of the events.
This motion is used to attempt to remove any motion blur in the event image. We
then propose a loss function applied to the motion compensated event image that
measures the motion blur in this image. We train two networks with this
framework, one to predict optical flow, and one to predict egomotion and
depths, and evaluate these networks on the Multi Vehicle Stereo Event Camera
dataset, along with qualitative results from a variety of different scenes.Comment: 9 pages, 7 figure
Domain-Indexing Variational Bayes: Interpretable Domain Index for Domain Adaptation
Previous studies have shown that leveraging domain index can significantly
boost domain adaptation performance (arXiv:2007.01807, arXiv:2202.03628).
However, such domain indices are not always available. To address this
challenge, we first provide a formal definition of domain index from the
probabilistic perspective, and then propose an adversarial variational Bayesian
framework that infers domain indices from multi-domain data, thereby providing
additional insight on domain relations and improving domain adaptation
performance. Our theoretical analysis shows that our adversarial variational
Bayesian framework finds the optimal domain index at equilibrium. Empirical
results on both synthetic and real data verify that our model can produce
interpretable domain indices which enable us to achieve superior performance
compared to state-of-the-art domain adaptation methods. Code is available at
https://github.com/Wang-ML-Lab/VDI.Comment: ICLR 2023 Spotlight (notable-top-25%
Crocs: Cross-Technology Clock Synchronization for WiFi and ZigBee
Clock synchronization is a key function in embedded wireless systems and
networks. This issue is equally important and more challenging in IoT systems
nowadays, which often include heterogeneous wireless devices that follow
different wireless standards. Conventional solutions to this problem employ
gateway-based indirect synchronization, which suffers low accuracy. This paper
for the first time studies the problem of cross-technology clock
synchronization. Our proposal called Crocs synchronizes WiFi and ZigBee devices
by direct cross-technology communication. Crocs decouples the synchronization
signal from the transmission of a timestamp. By incorporating a barker-code
based beacon for time alignment and cross-technology transmission of
timestamps, Crocs achieves robust and accurate synchronization among WiFi and
ZigBee devices, with the synchronization error lower than 1 millisecond. We
further make attempts to implement different cross-technology communication
methods in Crocs and provide insight findings with regard to the achievable
accuracy and expected overhead
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