2,169 research outputs found
Poisson multi-Bernoulli conjugate prior for multiple extended object filtering
This paper presents a Poisson multi-Bernoulli mixture (PMBM) conjugate prior
for multiple extended object filtering. A Poisson point process is used to
describe the existence of yet undetected targets, while a multi-Bernoulli
mixture describes the distribution of the targets that have been detected. The
prediction and update equations are presented for the standard transition
density and measurement likelihood. Both the prediction and the update preserve
the PMBM form of the density, and in this sense the PMBM density is a conjugate
prior. However, the unknown data associations lead to an intractably large
number of terms in the PMBM density, and approximations are necessary for
tractability. A gamma Gaussian inverse Wishart implementation is presented,
along with methods to handle the data association problem. A simulation study
shows that the extended target PMBM filter performs well in comparison to the
extended target d-GLMB and LMB filters. An experiment with Lidar data
illustrates the benefit of tracking both detected and undetected targets
Galaxia: a code to generate a synthetic survey of the Milky Way
We present here a fast code for creating a synthetic survey of the Milky Way.
Given one or more color-magnitude bounds, a survey size and geometry, the code
returns a catalog of stars in accordance with a given model of the Milky Way.
The model can be specified by a set of density distributions or as an N-body
realization. We provide fast and efficient algorithms for sampling both types
of models. As compared to earlier sampling schemes which generate stars at
specified locations along a line of sight, our scheme can generate a continuous
and smooth distribution of stars over any given volume. The code is quite
general and flexible and can accept input in the form of a star formation rate,
age metallicity relation, age velocity dispersion relation and analytic density
distribution functions. Theoretical isochrones are then used to generate a
catalog of stars and support is available for a wide range of photometric
bands. As a concrete example we implement the Besancon Milky Way model for the
disc. For the stellar halo we employ the simulated stellar halo N-body models
of Bullock & Johnston (2005). In order to sample N-body models, we present a
scheme that disperses the stars spawned by an N-body particle, in such a way
that the phase space density of the spawned stars is consistent with that of
the N-body particles. The code is ideally suited to generating synthetic data
sets that mimic near future wide area surveys such as GAIA, LSST and HERMES. As
an application we study the prospect of identifying structures in the stellar
halo with a simulated GAIA survey. We plan to make the code publicly available
at http://galaxia.sourceforge.net.Comment: Accepted for publication in Ap
Complexer-YOLO: Real-Time 3D Object Detection and Tracking on Semantic Point Clouds
Accurate detection of 3D objects is a fundamental problem in computer vision
and has an enormous impact on autonomous cars, augmented/virtual reality and
many applications in robotics. In this work we present a novel fusion of neural
network based state-of-the-art 3D detector and visual semantic segmentation in
the context of autonomous driving. Additionally, we introduce
Scale-Rotation-Translation score (SRTs), a fast and highly parameterizable
evaluation metric for comparison of object detections, which speeds up our
inference time up to 20\% and halves training time. On top, we apply
state-of-the-art online multi target feature tracking on the object
measurements to further increase accuracy and robustness utilizing temporal
information. Our experiments on KITTI show that we achieve same results as
state-of-the-art in all related categories, while maintaining the performance
and accuracy trade-off and still run in real-time. Furthermore, our model is
the first one that fuses visual semantic with 3D object detection
A second-order PHD filter with mean and variance in target number
The Probability Hypothesis Density (PHD) and Cardinalized PHD (CPHD) filters
are popular solutions to the multi-target tracking problem due to their low
complexity and ability to estimate the number and states of targets in
cluttered environments. The PHD filter propagates the first-order moment (i.e.
mean) of the number of targets while the CPHD propagates the cardinality
distribution in the number of targets, albeit for a greater computational cost.
Introducing the Panjer point process, this paper proposes a second-order PHD
filter, propagating the second-order moment (i.e. variance) of the number of
targets alongside its mean. The resulting algorithm is more versatile in the
modelling choices than the PHD filter, and its computational cost is
significantly lower compared to the CPHD filter. The paper compares the three
filters in statistical simulations which demonstrate that the proposed filter
reacts more quickly to changes in the number of targets, i.e., target births
and target deaths, than the CPHD filter. In addition, a new statistic for
multi-object filters is introduced in order to study the correlation between
the estimated number of targets in different regions of the state space, and
propose a quantitative analysis of the spooky effect for the three filters
Ordered Navigation on Multi-attributed Data Words
We study temporal logics and automata on multi-attributed data words.
Recently, BD-LTL was introduced as a temporal logic on data words extending LTL
by navigation along positions of single data values. As allowing for navigation
wrt. tuples of data values renders the logic undecidable, we introduce ND-LTL,
an extension of BD-LTL by a restricted form of tuple-navigation. While complete
ND-LTL is still undecidable, the two natural fragments allowing for either
future or past navigation along data values are shown to be Ackermann-hard, yet
decidability is obtained by reduction to nested multi-counter systems. To this
end, we introduce and study nested variants of data automata as an intermediate
model simplifying the constructions. To complement these results we show that
imposing the same restrictions on BD-LTL yields two 2ExpSpace-complete
fragments while satisfiability for the full logic is known to be as hard as
reachability in Petri nets
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