287,430 research outputs found
Beyond Correlation Filters: Learning Continuous Convolution Operators for Visual Tracking
Discriminative Correlation Filters (DCF) have demonstrated excellent
performance for visual object tracking. The key to their success is the ability
to efficiently exploit available negative data by including all shifted
versions of a training sample. However, the underlying DCF formulation is
restricted to single-resolution feature maps, significantly limiting its
potential. In this paper, we go beyond the conventional DCF framework and
introduce a novel formulation for training continuous convolution filters. We
employ an implicit interpolation model to pose the learning problem in the
continuous spatial domain. Our proposed formulation enables efficient
integration of multi-resolution deep feature maps, leading to superior results
on three object tracking benchmarks: OTB-2015 (+5.1% in mean OP), Temple-Color
(+4.6% in mean OP), and VOT2015 (20% relative reduction in failure rate).
Additionally, our approach is capable of sub-pixel localization, crucial for
the task of accurate feature point tracking. We also demonstrate the
effectiveness of our learning formulation in extensive feature point tracking
experiments. Code and supplementary material are available at
http://www.cvl.isy.liu.se/research/objrec/visualtracking/conttrack/index.html.Comment: Accepted at ECCV 201
Unveiling the Power of Deep Tracking
In the field of generic object tracking numerous attempts have been made to
exploit deep features. Despite all expectations, deep trackers are yet to reach
an outstanding level of performance compared to methods solely based on
handcrafted features. In this paper, we investigate this key issue and propose
an approach to unlock the true potential of deep features for tracking. We
systematically study the characteristics of both deep and shallow features, and
their relation to tracking accuracy and robustness. We identify the limited
data and low spatial resolution as the main challenges, and propose strategies
to counter these issues when integrating deep features for tracking.
Furthermore, we propose a novel adaptive fusion approach that leverages the
complementary properties of deep and shallow features to improve both
robustness and accuracy. Extensive experiments are performed on four
challenging datasets. On VOT2017, our approach significantly outperforms the
top performing tracker from the challenge with a relative gain of 17% in EAO
Efficient Feature Description for Small Body Relative Navigation using Binary Convolutional Neural Networks
Missions to small celestial bodies rely heavily on optical feature tracking
for characterization of and relative navigation around the target body. While
techniques for feature tracking based on deep learning are a promising
alternative to current human-in-the-loop processes, designing deep
architectures that can operate onboard spacecraft is challenging due to onboard
computational and memory constraints. This paper introduces a novel deep local
feature description architecture that leverages binary convolutional neural
network layers to significantly reduce computational and memory requirements.
We train and test our models on real images of small bodies from legacy and
ongoing missions and demonstrate increased performance relative to traditional
handcrafted methods. Moreover, we implement our models onboard a surrogate for
the next-generation spacecraft processor and demonstrate feasible runtimes for
online feature tracking.Comment: Presented at the 2023 AAS Guidance, Navigation and Control (GN&C)
Conference, February 2-8, 2023. arXiv admin note: text overlap with
arXiv:2208.0205
EchoFusion: Tracking and Reconstruction of Objects in 4D Freehand Ultrasound Imaging without External Trackers
Ultrasound (US) is the most widely used fetal imaging technique. However, US
images have limited capture range, and suffer from view dependent artefacts
such as acoustic shadows. Compounding of overlapping 3D US acquisitions into a
high-resolution volume can extend the field of view and remove image artefacts,
which is useful for retrospective analysis including population based studies.
However, such volume reconstructions require information about relative
transformations between probe positions from which the individual volumes were
acquired. In prenatal US scans, the fetus can move independently from the
mother, making external trackers such as electromagnetic or optical tracking
unable to track the motion between probe position and the moving fetus. We
provide a novel methodology for image-based tracking and volume reconstruction
by combining recent advances in deep learning and simultaneous localisation and
mapping (SLAM). Tracking semantics are established through the use of a
Residual 3D U-Net and the output is fed to the SLAM algorithm. As a proof of
concept, experiments are conducted on US volumes taken from a whole body fetal
phantom, and from the heads of real fetuses. For the fetal head segmentation,
we also introduce a novel weak annotation approach to minimise the required
manual effort for ground truth annotation. We evaluate our method
qualitatively, and quantitatively with respect to tissue discrimination
accuracy and tracking robustness.Comment: MICCAI Workshop on Perinatal, Preterm and Paediatric Image analysis
(PIPPI), 201
Below Horizon Aircraft Detection Using Deep Learning for Vision-Based Sense and Avoid
Commercial operation of unmanned aerial vehicles (UAVs) would benefit from an
onboard ability to sense and avoid (SAA) potential mid-air collision threats.
In this paper we present a new approach for detection of aircraft below the
horizon. We address some of the challenges faced by existing vision-based SAA
methods such as detecting stationary aircraft (that have no relative motion to
the background), rejecting moving ground vehicles, and simultaneous detection
of multiple aircraft. We propose a multi-stage, vision-based aircraft detection
system which utilises deep learning to produce candidate aircraft that we track
over time. We evaluate the performance of our proposed system on real flight
data where we demonstrate detection ranges comparable to the state of the art
with the additional capability of detecting stationary aircraft, rejecting
moving ground vehicles, and tracking multiple aircraft
Accurate Real Time Localization Tracking in A Clinical Environment using Bluetooth Low Energy and Deep Learning
Deep learning has started to revolutionize several different industries, and
the applications of these methods in medicine are now becoming more
commonplace. This study focuses on investigating the feasibility of tracking
patients and clinical staff wearing Bluetooth Low Energy (BLE) tags in a
radiation oncology clinic using artificial neural networks (ANNs) and
convolutional neural networks (CNNs). The performance of these networks was
compared to relative received signal strength indicator (RSSI) thresholding and
triangulation. By utilizing temporal information, a combined CNN+ANN network
was capable of correctly identifying the location of the BLE tag with an
accuracy of 99.9%. It outperformed a CNN model (accuracy = 94%), a thresholding
model employing majority voting (accuracy = 95%), and a triangulation
classifier utilizing majority voting (accuracy = 95%). Future studies will seek
to deploy this affordable real time location system in hospitals to improve
clinical workflow, efficiency, and patient safety
The goldstone real-time connected element interferometer
Connected element interferometry (CEI) is a technique of observing a celestial radio source at two spatially separated antennas and then interfering the received signals to extract the relative phase of the signal at the two antennas. The high precision of the resulting phase delay data type can provide an accurate determination of the angular position of the radio source relative to the baseline vector between the two stations. This article describes a recently developed connected element interferometer on a 21-km baseline between two antennas at the Deep Space Network's Goldstone, California, tracking complex. Fiber-optic links are used to transmit the data to a common site for processing. The system incorporates a real-time correlator to process these data in real time. The architecture of the system is described, and observational data are presented to characterize the potential performance of such a system. The real-time processing capability offers potential advantages in terms of increased reliability and improved delivery of navigational data for time-critical operations. Angular accuracies of 50-100 nrad are achievable on this baseline
Advanced Navigation Strategies For Asteroid Sample Return Missions
Flyby and rendezvous missions to asteroids have been accomplished using navigation techniques derived from experience gained in planetary exploration. This paper presents analysis of advanced navigation techniques required to meet unique challenges for precision navigation to acquire a sample from an asteroid and return it to Earth. These techniques rely on tracking data types such as spacecraft-based laser ranging and optical landmark tracking in addition to the traditional Earth-based Deep Space Network radio metric tracking. A systematic study of navigation strategy, including the navigation event timeline and reduction in spacecraft-asteroid relative errors, has been performed using simulation and covariance analysis on a representative mission
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