4,876 research outputs found
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
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
Fusion of Data from Heterogeneous Sensors with Distributed Fields of View and Situation Evaluation for Advanced Driver Assistance Systems
In order to develop a driver assistance system for pedestrian protection, pedestrians in the environment of a truck are detected by radars and a camera and are tracked across distributed fields of view using a Joint Integrated Probabilistic Data Association filter. A robust approach for prediction of the system vehicles trajectory is presented. It serves the computation of a probabilistic collision risk based on reachable sets where different sources of uncertainty are taken into account
Radar-based Application of Pedestrian and Cyclist Micro-Doppler Signatures for Automotive Safety Systems
Die sensorbasierte Erfassung des Nahfeldes im Kontext des hochautomatisierten Fahrens erfĂ€hrt einen spĂŒrbaren Trend bei der Integration von Radarsensorik. Fortschritte in der Mikroelektronik erlauben den Einsatz von hochauflösenden Radarsensoren, die durch effiziente Verfahren sowohl im Winkel als auch in der Entfernung und im Doppler die Messgenauigkeit kontinuierlich ansteigen lassen. Dadurch ergeben sich neuartige Möglichkeiten bei der Bestimmung der geometrischen und kinematischen Beschaffenheit ausgedehnter Ziele im Fahrzeugumfeld, die zur gezielten Entwicklung von automotiven Sicherheitssystemen herangezogen werden können.
Im Rahmen dieser Arbeit werden ungeschĂŒtzte Verkehrsteilnehmer wie FuĂgĂ€nger und Radfahrer mittels eines hochauflösenden Automotive-Radars analysiert. Dabei steht die Erscheinung des Mikro-Doppler-Effekts, hervorgerufen durch das hohe MaĂ an kinematischen Freiheitsgraden der Objekte, im Vordergrund der Betrachtung. Die durch den Mikro-Doppler-Effekt entstehenden charakteristischen Radar-Signaturen erlauben eine detailliertere Perzeption der Objekte und können in direkten Zusammenhang zu ihren aktuellen BewegungszustĂ€nden gesetzt werden. Es werden neuartige Methoden vorgestellt, die die geometrischen und kinematischen Ausdehnungen der Objekte berĂŒcksichtigen und echtzeitfĂ€hige AnsĂ€tze zur Klassifikation und Verhaltensindikation realisieren.
Wird ein ausgedehntes Ziel (z.B. Radfahrer) von einem Radarsensor detektiert, können aus dessen Mikro-Doppler-Signatur wesentliche Eigenschaften bezĂŒglich seines Bewegungszustandes innerhalb eines Messzyklus erfasst werden. Die Geschwindigkeitsverteilungen der sich drehenden RĂ€der erlauben eine adaptive Eingrenzung der Tretbewegung, deren Verhalten essentielle Merkmale im Hinblick auf eine vorausschauende UnfallprĂ€diktion aufweist. Ferner unterliegen ausgedehnte Radarziele einer OrientierungsabhĂ€ngigkeit, die deren geometrischen und kinematischen Profile direkt beeinflusst. Dies kann sich sowohl negativ auf die Klassifikations-Performance als auch auf die Verwertbarkeit von Parametern
auswirken, die eine Absichtsbekundung des Radarziels konstituieren. Am Beispiel des Radfahrers wird hierzu ein Verfahren vorgestellt, das die orientierungsabhÀngigen Parameter in Entfernung und Doppler normalisiert und die gemessenen Mehrdeutigkeiten kompensiert.
Ferner wird in dieser Arbeit eine Methodik vorgestellt, die auf Grundlage des Mikro-
Doppler-Profils eines FuĂgĂ€ngers dessen Beinbewegungen ĂŒber die Zeit schĂ€tzt (Tracking) und wertvolle Objektinformationen hinsichtlich seines Bewegungsverhaltens offenbart. Dazu wird ein Bewegungsmodell entwickelt, das die nichtlineare Fortbewegung des Beins approximiert und dessen hohes MaĂ an biomechanischer VariabilitĂ€t abbildet. Durch die Einbeziehung einer wahrscheinlichkeitsbasierten Datenassoziation werden die Radar-Detektionen ihren jeweils hervorrufenden Quellen (linkes und rechtes Bein) zugeordnet und
eine Trennung der GliedmaĂen realisiert. Im Gegensatz zu bisherigen Tracking-Verfahren weist die vorgestellte Methodik eine Steigerung in der Genauigkeit der Objektinformationen auf und stellt damit einen entscheidenden Vorteil fĂŒr zukĂŒnftige Fahrerassistenzsysteme dar, um deutlich schneller auf kritische Verkehrssituationen reagieren zu können.:1 Introduction 1
1.1 Automotive environmental perception 2
1.2 Contributions of this work 4
1.3 Thesis overview 6
2 Automotive radar 9
2.1 Physical fundamentals 9
2.1.1 Radar cross section 9
2.1.2 Radar equation 10
2.1.3 Micro-Doppler effect 11
2.2 Radar measurement model 15
2.2.1 FMCW radar 15
2.2.2 Chirp sequence modulation 17
2.2.3 Direction-of-arrival estimation 22
2.3 Signal processing 25
2.3.1 Target properties 26
2.3.2 Target extraction 28
Power detection 28
Clustering 30
2.3.3 Real radar data example 31
2.4 Conclusion 33
3 Micro-Doppler applications of a cyclist 35
3.1 Physical fundamentals 35
3.1.1 Micro-Doppler signatures of a cyclist 35
3.1.2 Orientation dependence 36
3.2 Cyclist feature extraction 38
3.2.1 Adaptive pedaling extraction 38
Ellipticity constraints 38
Ellipse fitting algorithm 39
3.2.2 Experimental results 42
3.3 Normalization of the orientation dependence 44
3.3.1 Geometric correction 44
3.3.2 Kinematic correction 45
3.3.3 Experimental results 45
3.4 Conclusion 47
3.5 Discussion and outlook 47
4 Micro-Doppler applications of a pedestrian 49
4.1 Pedestrian detection 49
4.1.1 Human kinematics 49
4.1.2 Micro-Doppler signatures of a pedestrian 51
4.1.3 Experimental results 52
Radially moving pedestrian 52
Crossing pedestrian 54
4.2 Pedestrian feature extraction 57
4.2.1 Frequency-based limb separation 58
4.2.2 Extraction of body parts 60
4.2.3 Experimental results 62
4.3 Pedestrian tracking 64
4.3.1 Probabilistic state estimation 65
4.3.2 Gaussian filters 67
4.3.3 The Kalman filter 67
4.3.4 The extended Kalman filter 69
4.3.5 Multiple-object tracking 71
4.3.6 Data association 74
4.3.7 Joint probabilistic data association 80
4.4 Kinematic-based pedestrian tracking 84
4.4.1 Kinematic modeling 84
4.4.2 Tracking motion model 87
4.4.3 4-D radar point cloud 91
4.4.4 Tracking implementation 92
4.4.5 Experimental results 96
Longitudinal trajectory 96
Crossing trajectory with sudden turn 98
4.5 Conclusion 102
4.6 Discussion and outlook 103
5 Summary and outlook 105
5.1 Developed algorithms 105
5.1.1 Adaptive pedaling extraction 105
5.1.2 Normalization of the orientation dependence 105
5.1.3 Model-based pedestrian tracking 106
5.2 Outlook 106
Bibliography 109
List of Acronyms 119
List of Figures 124
List of Tables 125
Appendix 127
A Derivation of the rotation matrix 2.26 127
B Derivation of the mixed radar signal 2.52 129
C Calculation of the marginal association probabilities 4.51 131
Curriculum Vitae 135Sensor-based detection of the near field in the context of highly automated driving is experiencing a noticeable trend in the integration of radar sensor technology. Advances in
microelectronics allow the use of high-resolution radar sensors that continuously increase measurement accuracy through efficient processes in angle as well as distance and Doppler.
This opens up novel possibilities in determining the geometric and kinematic nature of extended targets in the vehicle environment, which can be used for the specific development
of automotive safety systems.
In this work, vulnerable road users such as pedestrians and cyclists are analyzed using a high-resolution automotive radar. The focus is on the appearance of the micro-Doppler
effect, caused by the objectsâ high kinematic degree of freedom. The characteristic radar signatures produced by the micro-Doppler effect allow a clearer perception of the objects
and can be directly related to their current state of motion. Novel methods are presented that consider the geometric and kinematic extents of the objects and realize real-time
approaches to classification and behavioral indication.
When a radar sensor detects an extended target (e.g., bicyclist), its motion stateâs fundamental properties can be captured from its micro-Doppler signature within a measurement
cycle. The spinning wheelsâ velocity distributions allow an adaptive containment of the pedaling motion, whose behavior exhibits essential characteristics concerning predictive
accident prediction. Furthermore, extended radar targets are subject to orientation dependence, directly affecting their geometric and kinematic profiles. This can negatively affect
both the classification performance and the usability of parameters constituting the radar targetâs intention statement. For this purpose, using the cyclist as an example, a method
is presented that normalizes the orientation-dependent parameters in range and Doppler and compensates for the measured ambiguities.
Furthermore, this paper presents a methodology that estimates a pedestrianâs leg motion over time (tracking) based on the pedestrianâs micro-Doppler profile and reveals valuable
object information regarding his motion behavior. To this end, a motion model is developed that approximates the legâs nonlinear locomotion and represents its high degree of biomechanical variability. By incorporating likelihood-based data association, radar detections are assigned to their respective evoking sources (left and right leg), and limb separation is
realized. In contrast to previous tracking methods, the presented methodology shows an increase in the object informationâs accuracy. It thus represents a decisive advantage for
future driver assistance systems in order to be able to react significantly faster to critical traffic situations.:1 Introduction 1
1.1 Automotive environmental perception 2
1.2 Contributions of this work 4
1.3 Thesis overview 6
2 Automotive radar 9
2.1 Physical fundamentals 9
2.1.1 Radar cross section 9
2.1.2 Radar equation 10
2.1.3 Micro-Doppler effect 11
2.2 Radar measurement model 15
2.2.1 FMCW radar 15
2.2.2 Chirp sequence modulation 17
2.2.3 Direction-of-arrival estimation 22
2.3 Signal processing 25
2.3.1 Target properties 26
2.3.2 Target extraction 28
Power detection 28
Clustering 30
2.3.3 Real radar data example 31
2.4 Conclusion 33
3 Micro-Doppler applications of a cyclist 35
3.1 Physical fundamentals 35
3.1.1 Micro-Doppler signatures of a cyclist 35
3.1.2 Orientation dependence 36
3.2 Cyclist feature extraction 38
3.2.1 Adaptive pedaling extraction 38
Ellipticity constraints 38
Ellipse fitting algorithm 39
3.2.2 Experimental results 42
3.3 Normalization of the orientation dependence 44
3.3.1 Geometric correction 44
3.3.2 Kinematic correction 45
3.3.3 Experimental results 45
3.4 Conclusion 47
3.5 Discussion and outlook 47
4 Micro-Doppler applications of a pedestrian 49
4.1 Pedestrian detection 49
4.1.1 Human kinematics 49
4.1.2 Micro-Doppler signatures of a pedestrian 51
4.1.3 Experimental results 52
Radially moving pedestrian 52
Crossing pedestrian 54
4.2 Pedestrian feature extraction 57
4.2.1 Frequency-based limb separation 58
4.2.2 Extraction of body parts 60
4.2.3 Experimental results 62
4.3 Pedestrian tracking 64
4.3.1 Probabilistic state estimation 65
4.3.2 Gaussian filters 67
4.3.3 The Kalman filter 67
4.3.4 The extended Kalman filter 69
4.3.5 Multiple-object tracking 71
4.3.6 Data association 74
4.3.7 Joint probabilistic data association 80
4.4 Kinematic-based pedestrian tracking 84
4.4.1 Kinematic modeling 84
4.4.2 Tracking motion model 87
4.4.3 4-D radar point cloud 91
4.4.4 Tracking implementation 92
4.4.5 Experimental results 96
Longitudinal trajectory 96
Crossing trajectory with sudden turn 98
4.5 Conclusion 102
4.6 Discussion and outlook 103
5 Summary and outlook 105
5.1 Developed algorithms 105
5.1.1 Adaptive pedaling extraction 105
5.1.2 Normalization of the orientation dependence 105
5.1.3 Model-based pedestrian tracking 106
5.2 Outlook 106
Bibliography 109
List of Acronyms 119
List of Figures 124
List of Tables 125
Appendix 127
A Derivation of the rotation matrix 2.26 127
B Derivation of the mixed radar signal 2.52 129
C Calculation of the marginal association probabilities 4.51 131
Curriculum Vitae 13
Human Motion Trajectory Prediction: A Survey
With growing numbers of intelligent autonomous systems in human environments,
the ability of such systems to perceive, understand and anticipate human
behavior becomes increasingly important. Specifically, predicting future
positions of dynamic agents and planning considering such predictions are key
tasks for self-driving vehicles, service robots and advanced surveillance
systems. This paper provides a survey of human motion trajectory prediction. We
review, analyze and structure a large selection of work from different
communities and propose a taxonomy that categorizes existing methods based on
the motion modeling approach and level of contextual information used. We
provide an overview of the existing datasets and performance metrics. We
discuss limitations of the state of the art and outline directions for further
research.Comment: Submitted to the International Journal of Robotics Research (IJRR),
37 page
Implementation and Evaluation of a Cooperative Vehicle-to-Pedestrian Safety Application
While the development of Vehicle-to-Vehicle (V2V) safety applications based
on Dedicated Short-Range Communications (DSRC) has been extensively undergoing
standardization for more than a decade, such applications are extremely missing
for Vulnerable Road Users (VRUs). Nonexistence of collaborative systems between
VRUs and vehicles was the main reason for this lack of attention. Recent
developments in Wi-Fi Direct and DSRC-enabled smartphones are changing this
perspective. Leveraging the existing V2V platforms, we propose a new framework
using a DSRC-enabled smartphone to extend safety benefits to VRUs. The
interoperability of applications between vehicles and portable DSRC enabled
devices is achieved through the SAE J2735 Personal Safety Message (PSM).
However, considering the fact that VRU movement dynamics, response times, and
crash scenarios are fundamentally different from vehicles, a specific framework
should be designed for VRU safety applications to study their performance. In
this article, we first propose an end-to-end Vehicle-to-Pedestrian (V2P)
framework to provide situational awareness and hazard detection based on the
most common and injury-prone crash scenarios. The details of our VRU safety
module, including target classification and collision detection algorithms, are
explained next. Furthermore, we propose and evaluate a mitigating solution for
congestion and power consumption issues in such systems. Finally, the whole
system is implemented and analyzed for realistic crash scenarios
Multiple Target, Multiple Type Filtering in the RFS Framework
A Multiple Target, Multiple Type Filtering (MTMTF) algorithm is developed
using Random Finite Set (RFS) theory. First, we extend the standard Probability
Hypothesis Density (PHD) filter for multiple types of targets, each with
distinct detection properties, to develop a multiple target, multiple type
filtering, N-type PHD filter, where , for handling confusions among
target types. In this approach, we assume that there will be confusions between
detections, i.e. clutter arises not just from background false positives, but
also from target confusions. Then, under the assumptions of Gaussianity and
linearity, we extend the Gaussian mixture (GM) implementation of the standard
PHD filter for the proposed N-type PHD filter termed the N-type GM-PHD filter.
Furthermore, we analyze the results from simulations to track sixteen targets
of four different types using a four-type (quad) GM-PHD filter as a typical
example and compare it with four independent GM-PHD filters using the Optimal
Subpattern Assignment (OSPA) metric. This shows the improved performance of our
strategy that accounts for target confusions by efficiently discriminating
them
Utilising Visual Attention Cues for Vehicle Detection and Tracking
Advanced Driver-Assistance Systems (ADAS) have been attracting attention from
many researchers. Vision-based sensors are the closest way to emulate human
driver visual behavior while driving. In this paper, we explore possible ways
to use visual attention (saliency) for object detection and tracking. We
investigate: 1) How a visual attention map such as a \emph{subjectness}
attention or saliency map and an \emph{objectness} attention map can facilitate
region proposal generation in a 2-stage object detector; 2) How a visual
attention map can be used for tracking multiple objects. We propose a neural
network that can simultaneously detect objects as and generate objectness and
subjectness maps to save computational power. We further exploit the visual
attention map during tracking using a sequential Monte Carlo probability
hypothesis density (PHD) filter. The experiments are conducted on KITTI and
DETRAC datasets. The use of visual attention and hierarchical features has
shown a considerable improvement of 8\% in object detection which
effectively increased tracking performance by 4\% on KITTI dataset.Comment: Accepted in ICPR202
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