16,784 research outputs found
A Bayesian Filtering Algorithm for Gaussian Mixture Models
A Bayesian filtering algorithm is developed for a class of state-space
systems that can be modelled via Gaussian mixtures. In general, the exact
solution to this filtering problem involves an exponential growth in the number
of mixture terms and this is handled here by utilising a Gaussian mixture
reduction step after both the time and measurement updates. In addition, a
square-root implementation of the unified algorithm is presented and this
algorithm is profiled on several simulated systems. This includes the state
estimation for two non-linear systems that are strictly outside the class
considered in this paper
Marginal multi-Bernoulli filters: RFS derivation of MHT, JIPDA and association-based MeMBer
Recent developments in random finite sets (RFSs) have yielded a variety of
tracking methods that avoid data association. This paper derives a form of the
full Bayes RFS filter and observes that data association is implicitly present,
in a data structure similar to MHT. Subsequently, algorithms are obtained by
approximating the distribution of associations. Two algorithms result: one
nearly identical to JIPDA, and another related to the MeMBer filter. Both
improve performance in challenging environments.Comment: Journal version at http://ieeexplore.ieee.org/document/7272821.
Matlab code of simple implementation included with ancillary file
Recovering 6D Object Pose and Predicting Next-Best-View in the Crowd
Object detection and 6D pose estimation in the crowd (scenes with multiple
object instances, severe foreground occlusions and background distractors), has
become an important problem in many rapidly evolving technological areas such
as robotics and augmented reality. Single shot-based 6D pose estimators with
manually designed features are still unable to tackle the above challenges,
motivating the research towards unsupervised feature learning and
next-best-view estimation. In this work, we present a complete framework for
both single shot-based 6D object pose estimation and next-best-view prediction
based on Hough Forests, the state of the art object pose estimator that
performs classification and regression jointly. Rather than using manually
designed features we a) propose an unsupervised feature learnt from
depth-invariant patches using a Sparse Autoencoder and b) offer an extensive
evaluation of various state of the art features. Furthermore, taking advantage
of the clustering performed in the leaf nodes of Hough Forests, we learn to
estimate the reduction of uncertainty in other views, formulating the problem
of selecting the next-best-view. To further improve pose estimation, we propose
an improved joint registration and hypotheses verification module as a final
refinement step to reject false detections. We provide two additional
challenging datasets inspired from realistic scenarios to extensively evaluate
the state of the art and our framework. One is related to domestic environments
and the other depicts a bin-picking scenario mostly found in industrial
settings. We show that our framework significantly outperforms state of the art
both on public and on our datasets.Comment: CVPR 2016 accepted paper, project page:
http://www.iis.ee.ic.ac.uk/rkouskou/6D_NBV.htm
Catalog Matching with Astrometric Correction and its Application to the Hubble Legacy Archive
Object cross-identification in multiple observations is often complicated by
the uncertainties in their astrometric calibration. Due to the lack of standard
reference objects, an image with a small field of view can have significantly
larger errors in its absolute positioning than the relative precision of the
detected sources within. We present a new general solution for the relative
astrometry that quickly refines the World Coordinate System of overlapping
fields. The efficiency is obtained through the use of infinitesimal 3-D
rotations on the celestial sphere, which do not involve trigonometric
functions. They also enable an analytic solution to an important step in making
the astrometric corrections. In cases with many overlapping images, the correct
identification of detections that match together across different images is
difficult to determine. We describe a new greedy Bayesian approach for
selecting the best object matches across a large number of overlapping images.
The methods are developed and demonstrated on the Hubble Legacy Archive, one of
the most challenging data sets today. We describe a novel catalog compiled from
many Hubble Space Telescope observations, where the detections are combined
into a searchable collection of matches that link the individual detections.
The matches provide descriptions of astronomical objects involving multiple
wavelengths and epochs. High relative positional accuracy of objects is
achieved across the Hubble images, often sub-pixel precision in the order of
just a few milli-arcseconds. The result is a reliable set of high-quality
associations that are publicly available online.Comment: 9 pages, 9 figures, accepted for publication in the Astrophysical
Journa
Bayesian Multiple Hypothesis Tracking of Merging and Splitting Targets
International audienceThis paper presents a Bayesian model for the multiple target tracking problem that handles a varying number of splitting and merging targets applied to convective cloud tracking. The model decomposes the tracking solution into events and targets state. The events include target births, deaths, splits, and merges. The target state contains both the target positions and attributes. By updating the target attributes and conditioning the events on their updated values we can include high level domain knowledge into the system. This strategy improves the tracking accuracy and the computational efficiency since we focus only on likely events for each situation. A two-step multiple hypothesis tracking algorithm has been developed to estimate the model state. The proposed approach is tested by both simulation and real data for mesoscale convective systems tracking
A Survey of Recent Advances in Particle Filters and Remaining Challenges for Multitarget Tracking
[EN]We review some advances of the particle filtering (PF) algorithm that have been achieved
in the last decade in the context of target tracking, with regard to either a single target or multiple
targets in the presence of false or missing data. The first part of our review is on remarkable
achievements that have been made for the single-target PF from several aspects including importance
proposal, computing efficiency, particle degeneracy/impoverishment and constrained/multi-modal
systems. The second part of our review is on analyzing the intractable challenges raised within
the general multitarget (multi-sensor) tracking due to random target birth and termination, false
alarm, misdetection, measurement-to-track (M2T) uncertainty and track uncertainty. The mainstream
multitarget PF approaches consist of two main classes, one based on M2T association approaches and
the other not such as the finite set statistics-based PF. In either case, significant challenges remain due
to unknown tracking scenarios and integrated tracking management
Wavelet-Fourier CORSING techniques for multi-dimensional advection-diffusion-reaction equations
We present and analyze a novel wavelet-Fourier technique for the numerical
treatment of multidimensional advection-diffusion-reaction equations based on
the CORSING (COmpRessed SolvING) paradigm. Combining the Petrov-Galerkin
technique with the compressed sensing approach, the proposed method is able to
approximate the largest coefficients of the solution with respect to a
biorthogonal wavelet basis. Namely, we assemble a compressed discretization
based on randomized subsampling of the Fourier test space and we employ sparse
recovery techniques to approximate the solution to the PDE. In this paper, we
provide the first rigorous recovery error bounds and effective recipes for the
implementation of the CORSING technique in the multi-dimensional setting. Our
theoretical analysis relies on new estimates for the local a-coherence, which
measures interferences between wavelet and Fourier basis functions with respect
to the metric induced by the PDE operator. The stability and robustness of the
proposed scheme is shown by numerical illustrations in the one-, two-, and
three-dimensional case
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