206 research outputs found
Environment Perception Framework Fusing Multi-Object Tracking, Dynamic Occupancy Grid Maps and Digital Maps
Autonomously driving vehicles require a complete and robust perception of the
local environment. A main challenge is to perceive any other road users, where
multi-object tracking or occupancy grid maps are commonly used. The presented
approach combines both methods to compensate false positives and receive a
complementary environment perception. Therefore, an environment perception
framework is introduced that defines a common representation, extracts objects
from a dynamic occupancy grid map and fuses them with tracks of a Labeled
Multi-Bernoulli filter. Finally, a confidence value is developed, that
validates object estimates using different constraints regarding physical
possibilities, method specific characteristics and contextual information from
a digital map. Experimental results with real world data highlight the
robustness and significance of the presented fusing approach, utilizing the
confidence value in rural and urban scenarios
An Online Solution for Localisation, Tracking and Separation of Moving Speech Sources
The problem of separating a time varying number of speech sources in a room is difficult to solve. The challenge lies in estimating the number and the location of these speech sources. Furthermore, the tracked speech sources need to be separated. This thesis proposes a solution which utilises the Random Finite Set approach to estimate the number and location of these speech sources and subsequently separate the speech source mixture via time frequency masking
A Comprehensive Mapping and Real-World Evaluation of Multi-Object Tracking on Automated Vehicles
Multi-Object Tracking (MOT) is a field critical to Automated Vehicle (AV) perception systems. However, it is large, complex, spans research fields, and lacks resources for integration with real sensors and implementation on AVs. Factors such those make it difficult for new researchers and practitioners to enter the field.
This thesis presents two main contributions: 1) a comprehensive mapping for the field of Multi-Object Trackers (MOTs) with a specific focus towards Automated Vehicles (AVs) and 2) a real-world evaluation of an MOT developed and tuned using COTS (Commercial Off-The-Shelf) software toolsets. The first contribution aims to give a comprehensive overview of MOTs and various MOT subfields for AVs that have not been presented as wholistically in other papers. The second contribution aims to illustrate some of the benefits of using a COTS MOT toolset and some of the difficulties associated with using real-world data. This MOT performed accurate state estimation of a target vehicle through the tracking and fusion of data from a radar and vision sensor using a Central-Level Track Processing approach and a Global Nearest Neighbors assignment algorithm. It had an 0.44 m positional Root Mean Squared Error (RMSE) over a 40 m approach test.
It is the authors\u27 hope that this work provides an overview of the MOT field that will help new researchers and practitioners enter the field. Additionally, the author hopes that the evaluation section illustrates some difficulties of using real-world data and provides a good pathway for developing and deploying MOTs from software toolsets to Automated Vehicles
Tracking and Fusion Methods for Extended Targets Parameterized by Center, Orientation, and Semi-axes
The improvements in sensor technology, e.g., the development of automotive Radio Detection and
Ranging (RADAR) or Light Detection and Ranging (LIDAR), which are able to provide a higher
detail of the sensor’s environment, have introduced new opportunities but also new challenges to
target tracking. In classic target tracking, targets are assumed as points. However, this assumption
is no longer valid if targets occupy more than one sensor resolution cell, creating the need for
extended targets, modeling the shape in addition to the kinematic parameters. Different shape
models are possible and this thesis focuses on an elliptical shape, parameterized with center,
orientation, and semi-axes lengths. This parameterization can be used to model rectangles as well.
Furthermore, this thesis is concerned with multi-sensor fusion for extended targets, which can be
used to improve the target tracking by providing information gathered from different sensors or
perspectives. We also consider estimation of extended targets, i.e., to account for uncertainties, the
target is modeled by a probability density, so we need to find a so-called point estimate.
Extended target tracking provides a variety of challenges due to the spatial extent, which need
to be handled, even for basic shapes like ellipses and rectangles. Among these challenges are the
choice of the target model, e.g., how the measurements are distributed across the shape. Additional
challenges arise for sensor fusion, as it is unclear how to best consider the geometric properties
when combining two extended targets. Finally, the extent needs to be involved in the estimation.
Traditional methods often use simple uniform distributions across the shape, which do not properly
portray reality, while more complex methods require the use of optimization techniques or large
amounts of data. In addition, for traditional estimation, metrics such as the Euclidean distance
between state vectors are used. However, they might no longer be valid because they do not
consider the geometric properties of the targets’ shapes, e.g., rotating an ellipse by 180 degree
results in the same ellipse, but the Euclidean distance between them is not 0. In multi-sensor fusion,
the same holds, i.e., simply combining the corresponding elements of the state vectors can lead to
counter-intuitive fusion results.
In this work, we compare different elliptic trackers and discuss more complex measurement
distributions across the shape’s surface or contour. Furthermore, we discuss the problems which
can occur when fusing extended target estimates from different sensors and how to handle them
by providing a transformation into a special density. We then proceed to discuss how a different
metric, namely the Gaussian Wasserstein (GW) distance, can be used to improve target estimation.
We define an estimator and propose an approximation based on an extension of the square root
distance. It can be applied on the posterior densities of the aforementioned trackers to incorporate
the unique properties of ellipses in the estimation process. We also discuss how this can be applied
to rectangular targets as well. Finally, we evaluate and discuss our approaches. We show the
benefits of more complex target models in simulations and on real data and we demonstrate our
estimation and fusion approaches compared to classic methods on simulated data.2022-01-2
Multi-sensor multi-target tracking using domain knowledge and clustering
This paper proposes a novel joint multi-target tracking and track maintenance algorithm over a sensor network. Each sensor runs a local joint probabilistic data association (JPDA) filter using only its own measurements. Unlike the original JPDA approach, the proposed local filter utilises the detection amplitude as domain knowledge to improve the estimation accuracy. In the fusion stage, the DBSCAN clustering in conjunction with statistical test is proposed to group all local tracks into several clusters. Each generated cluster represents the local tracks that are from the same target source and the global estimation of each cluster is obtained by the generalized covariance intersection (GCI) algorithm. Extensive simulation results clearly confirms the effectiveness of the proposed multisensor multi-target tracking algorithm
Online Audio-Visual Multi-Source Tracking and Separation: A Labeled Random Finite Set Approach
The dissertation proposes an online solution for separating an unknown and time-varying number of moving sources using audio and visual data. The random finite set framework is used for the modeling and fusion of audio and visual data. This enables an online tracking algorithm to estimate the source positions and identities for each time point. With this information, a set of beamformers can be designed to separate each desired source and suppress the interfering sources
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