107,921 research outputs found
Deterministic Multi-sensor Measurement-adaptive Birth using Labeled Random Finite Sets
Measurement-adaptive track initiation remains a critical design requirement
of many practical multi-target tracking systems. For labeled random finite sets
multi-object filters, prior work has been established to construct a labeled
multi-object birth density using measurements from multiple sensors. A
truncation procedure has also been provided that leverages a stochastic Gibbs
sampler to truncate the birth density for scalability. In this work, we
introduce a deterministic herded Gibbs sampling truncation solution for
efficient multi-sensor adaptive track initialization. Removing the stochastic
behavior of the track initialization procedure without impacting average
tracking performance enables a more robust tracking solution more suitable for
safety-critical applications. Simulation results for linear sensing scenarios
are provided to verify performance.Comment: Accepted to the 2023 Proc. IEEE 26th Int. Conf. Inf. Fusio
Performance Evaluation of Random Set Based Pedestrian Tracking Algorithms
The paper evaluates the error performance of three random finite set based
multi-object trackers in the context of pedestrian video tracking. The
evaluation is carried out using a publicly available video dataset of 4500
frames (town centre street) for which the ground truth is available. The input
to all pedestrian tracking algorithms is an identical set of head and body
detections, obtained using the Histogram of Oriented Gradients (HOG) detector.
The tracking error is measured using the recently proposed OSPA metric for
tracks, adopted as the only known mathematically rigorous metric for measuring
the distance between two sets of tracks. A comparative analysis is presented
under various conditions.Comment: 6 pages, 3 figure
Extended Object Tracking: Introduction, Overview and Applications
This article provides an elaborate overview of current research in extended
object tracking. We provide a clear definition of the extended object tracking
problem and discuss its delimitation to other types of object tracking. Next,
different aspects of extended object modelling are extensively discussed.
Subsequently, we give a tutorial introduction to two basic and well used
extended object tracking approaches - the random matrix approach and the Kalman
filter-based approach for star-convex shapes. The next part treats the tracking
of multiple extended objects and elaborates how the large number of feasible
association hypotheses can be tackled using both Random Finite Set (RFS) and
Non-RFS multi-object trackers. The article concludes with a summary of current
applications, where four example applications involving camera, X-band radar,
light detection and ranging (lidar), red-green-blue-depth (RGB-D) sensors are
highlighted.Comment: 30 pages, 19 figure
Resilient Active Target Tracking with Multiple Robots
The problem of target tracking with multiple robots consists of actively
planning the motion of the robots to track the targets. A major challenge for
practical deployments is to make the robots resilient to failures. In
particular, robots may be attacked in adversarial scenarios, or their sensors
may fail or get occluded. In this paper, we introduce planning algorithms for
multi-target tracking that are resilient to such failures. In general,
resilient target tracking is computationally hard. Contrary to the case where
there are no failures, no scalable approximation algorithms are known for
resilient target tracking when the targets are indistinguishable, or unknown in
number, or with unknown motion model. In this paper we provide the first such
algorithm, that also has the following properties: First, it achieves maximal
resiliency, since the algorithm is valid for any number of failures. Second, it
is scalable, as our algorithm terminates with the same running time as
state-of-the-art algorithms for (non-resilient) target tracking. Third, it
provides provable approximation bounds on the tracking performance, since our
algorithm guarantees a solution that is guaranteed to be close to the optimal.
We quantify our algorithm's approximation performance using a novel notion of
curvature for monotone set functions subject to matroid constraints. Finally,
we demonstrate the efficacy of our algorithm through MATLAB and Gazebo
simulations, and a sensitivity analysis; we focus on scenarios that involve a
known number of distinguishable targets
Multiple target tracking based on sets of trajectories
We propose a solution of the multiple target tracking (MTT) problem based on
sets of trajectories and the random finite set framework. A full Bayesian
approach to MTT should characterise the distribution of the trajectories given
the measurements, as it contains all information about the trajectories. We
attain this by considering multi-object density functions in which objects are
trajectories. For the standard tracking models, we also describe a conjugate
family of multitrajectory density functions.Comment: MATLAB implementations of algorithms based on sets of trajectories
can be found at https://github.com/Agarciafernande
Poisson multi-Bernoulli mixture trackers: continuity through random finite sets of trajectories
The Poisson multi-Bernoulli mixture (PMBM) is an unlabelled multi-target
distribution for which the prediction and update are closed. It has a Poisson
birth process, and new Bernoulli components are generated on each new
measurement as a part of the Bayesian measurement update. The PMBM filter is
similar to the multiple hypothesis tracker (MHT), but seemingly does not
provide explicit continuity between time steps. This paper considers a recently
developed formulation of the multi-target tracking problem as a random finite
set (RFS) of trajectories, and derives two trajectory RFS filters, called PMBM
trackers. The PMBM trackers efficiently estimate the set of trajectories, and
share hypothesis structure with the PMBM filter. By showing that the prediction
and update in the PMBM filter can be viewed as an efficient method for
calculating the time marginals of the RFS of trajectories, continuity in the
same sense as MHT is established for the PMBM filter
Statistical Information Fusion for Multiple-View Sensor Data in Multi-Object Tracking
This paper presents a novel statistical information fusion method to
integrate multiple-view sensor data in multi-object tracking applications. The
proposed method overcomes the drawbacks of the commonly used Generalized
Covariance Intersection method, which considers constant weights allocated for
sensors. Our method is based on enhancing the Generalized Covariance
Intersection with adaptive weights that are automatically tuned based on the
amount of information carried by the measurements from each sensor. To quantify
information content, Cauchy-Schwarz divergence is used. Another distinguished
characteristic of our method lies in the usage of the Labeled Multi-Bernoulli
filter for multi-object tracking, in which the weight of each sensor can be
separately adapted for each Bernoulli component of the filter. The results of
numerical experiments show that our proposed method can successfully integrate
information provided by multiple sensors with different fields of view. In such
scenarios, our method significantly outperforms the state of art in terms of
inclusion of all existing objects and tracking accuracy.Comment: 28 pages,7 figure
Multi-Sensor Control for Multi-Target Tracking Using Cauchy-Schwarz Divergence
The paper addresses the problem of multi-sensor control for multi-target
tracking via labelled random finite sets (RFS) in the sensor network systems.
Based on an information theoretic divergence measure, namely Cauchy-Schwarz
(CS) divergence which admits a closed form solution for GLMB densities, we
propose two novel multi-sensor control approaches in the framework of
generalized Covariance Intersection (GCI). The first joint decision making
(JDM) method is optimal and can achieve overall good performance, while the
second independent decision making (IDM) method is suboptimal as a fast
realization with smaller amount of computations. Simulation in challenging
situation is presented to verify the effectiveness of the two proposed
approaches
Track Everything: Limiting Prior Knowledge in Online Multi-Object Recognition
This paper addresses the problem of online tracking and classification of
multiple objects in an image sequence. Our proposed solution is to first track
all objects in the scene without relying on object-specific prior knowledge,
which in other systems can take the form of hand-crafted features or user-based
track initialization. We then classify the tracked objects with a fast-learning
image classifier that is based on a shallow convolutional neural network
architecture and demonstrate that object recognition improves when this is
combined with object state information from the tracking algorithm. We argue
that by transferring the use of prior knowledge from the detection and tracking
stages to the classification stage we can design a robust, general purpose
object recognition system with the ability to detect and track a variety of
object types. We describe our biologically inspired implementation, which
adaptively learns the shape and motion of tracked objects, and apply it to the
Neovision2 Tower benchmark data set, which contains multiple object types. An
experimental evaluation demonstrates that our approach is competitive with
state-of-the-art video object recognition systems that do make use of
object-specific prior knowledge in detection and tracking, while providing
additional practical advantages by virtue of its generality.Comment: 15 page
A metric on the space of finite sets of trajectories for evaluation of multi-target tracking algorithms
In this paper, we propose a metric on the space of finite sets of
trajectories for assessing multi-target tracking algorithms in a mathematically
sound way. The main use of the metric is to compare estimates of trajectories
from different algorithms with the ground truth of trajectories. The proposed
metric includes intuitive costs associated to localization error for properly
detected targets, missed and false targets and track switches at each time
step. The metric computation is based on solving a multi-dimensional assignment
problem. We also propose a lower bound for the metric, which is also a metric
for sets of trajectories and is computable in polynomial time using linear
programming. We also extend the proposed metrics on sets of trajectories to
random finite sets of trajectories.Comment: Matlab code for the metric is available at
https://github.com/Agarciafernandez/MT
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