3,578 research outputs found
APPLICATION OF IMAGE ANALYSIS TECHNIQUES TO SATELLITE CLOUD MOTION TRACKING
Cloud motion wind (CMW) determination requires tracking of individual cloud targets.
This is achieved by first clustering and then tracking each cloud cluster. Ideally, different
cloud clusters correspond to diiferent pressure levels. Two new clustering techniques
have been developed for the identification of cloud types in multi-spectral satellite imagery.
The first technique is the Global-Local clustering algorithm. It is a cascade of a
histogram clustering algorithm and a dynamic clustering algorithm. The histogram
clustering algorithm divides the multi-spectral histogram into'non-overlapped regions,
and these regions are used to initialise the dynamic clustering algorithm. The dynamic
clustering algorithm assumes clusters have a Gaussian distributed probability density
function with diiferent population size and variance.
The second technique uses graph theory to exploit the spatial information which is
often ignored in per-pixel clustering. The algorithm is in two stages: spatial clustering
and spectral clustering. The first stage extracts homogeneous objects in the image
using a family of algorithms based on stepwise optimization. This family of algorithms
can be further divided into two approaches: Top-down and Bottom-up. The second
stage groups similar segments into clusters using a statistical hypothesis test on their
similarities. The clusters generated are less noisy along class boundaries and are in
hierarchical order. A criterion based on mutual information is derived to monitor the
spatial clustering process and to suggest an optimal number of segments.
An automated cloud motion tracking program has been developed. Three images
(each separated by 30 minutes) are used to track cloud motion and the middle image
is clustered using Global-Local clustering prior to tracking. Compared with traditional
methods based on raw images, it is found that separation of cloud types before cloud
tracking can reduce the ambiguity due to multi-layers of cloud moving at different
speeds and direction. Three matching techniques are used and their reliability compared.
Target sizes ranging from 4 x 4 to 32 x 32 are tested and their errors compared. The
optimum target size for first generation METEOSAT images has also been found.Meteorological Office, Bracknel
Analysis of infrared polarisation signatures for vehicle detection
Thermal radiation emitted from objects within a scene tends to be partially
polarised in a direction parallel to the surface normal, to an extent
governed by properties of the surface material. This thesis investigates
whether vehicle detection algorithms can be improved by the additional
measurement of polarisation state as well as intensity in the long wave
infrared.
Knowledge about the polarimetric properties of scenes guides the development
of histogram based and cluster based descriptors which are used
in a traditional classification framework. The best performing histogram
based method, the Polarimetric Histogram, which forms a descriptor
based on the polarimetric vehicle signature is shown to outperform the
standard Histogram of Oriented Gradients descriptor which uses intensity
imagery alone. These descriptors then lead to a novel clustering
algorithm which, at a false positive rate of 10−2 is shown to improve
upon the Polarimetric Histogram descriptor, increasing the true positive
rate from 0.19 to 0.63.
In addition, a multi-modal detection framework which combines thermal
intensity hotspot and polarimetric hotspot detections with a local motion
detector is presented. Through the combination of these detectors, the
false positive rate is shown to be reduced when compared to the result
of individual detectors in isolation
Recommended from our members
Real-time spatial modeling to detect and track resources on construction sites
For more than 10 years the U.S. construction industry has experienced over 1,000
fatalities annually. Many fatalities may have been prevented had the individuals and
equipment involved been more aware of and alert to the physical state of the environment
around them. Awareness may be improved by automatic 3D (three-dimensional) sensing
and modeling of the job site environment in real-time. Existing 3D modeling approaches
based on range scanning techniques are capable of modeling static objects only, and thus
cannot model in real-time dynamic objects in an environment comprised of moving
humans, equipment, and materials. Emerging prototype 3D video range cameras offer
another alternative by facilitating affordable, wide field of view, automated static and
dynamic object detection and tracking at frame rates better than 1Hz (real-time).
This dissertation presents an imperical work and methodology to rapidly create a
spatial model of construction sites and in particular to detect, model, and track the position, dimension, direction, and velocity of static and moving project resources in real-time, based on range data obtained from a three-dimensional video range camera in a
static or moving position. Existing construction site 3D modeling approaches based on
optical range sensing technologies (laser scanners, rangefinders, etc.) and 3D modeling
approaches (dense, sparse, etc.) that offered potential solutions for this research are
reviewed. The choice of an emerging sensing tool and preliminary experiments with this
prototype sensing technology are discussed. These findings led to the development of a
range data processing algorithm based on three-dimensional occupancy grids which is
demonstrated in detail. Testing and validation of the proposed algorithms have been
conducted to quantify the performance of sensor and algorithm through extensive
experimentation involving static and moving objects. Experiments in indoor laboratory
and outdoor construction environments have been conducted with construction resources
such as humans, equipment, materials, or structures to verify the accuracy of the
occupancy grid modeling approach. Results show that modeling objects and measuring
their position, dimension, direction, and speed had an accuracy level compatible to the
requirements of active safety features for construction. Results demonstrate that video
rate 3D data acquisition and analysis of construction environments can support effective
detection, tracking, and convex hull modeling of objects. Exploiting rapidly generated
three-dimensional models for improved visualization, communications, and process
control has inherent value, broad application, and potential impact, e.g. as-built vs. as-planned comparison, condition assessment, maintenance, operations, and construction
activities control. In combination with effective management practices, this sensing
approach has the potential to assist equipment operators to avoid incidents that result in
reduce human injury, death, or collateral damage on construction sites.Civil, Architectural, and Environmental Engineerin
Event-based Vision: A Survey
Event cameras are bio-inspired sensors that differ from conventional frame
cameras: Instead of capturing images at a fixed rate, they asynchronously
measure per-pixel brightness changes, and output a stream of events that encode
the time, location and sign of the brightness changes. Event cameras offer
attractive properties compared to traditional cameras: high temporal resolution
(in the order of microseconds), very high dynamic range (140 dB vs. 60 dB), low
power consumption, and high pixel bandwidth (on the order of kHz) resulting in
reduced motion blur. Hence, event cameras have a large potential for robotics
and computer vision in challenging scenarios for traditional cameras, such as
low-latency, high speed, and high dynamic range. However, novel methods are
required to process the unconventional output of these sensors in order to
unlock their potential. This paper provides a comprehensive overview of the
emerging field of event-based vision, with a focus on the applications and the
algorithms developed to unlock the outstanding properties of event cameras. We
present event cameras from their working principle, the actual sensors that are
available and the tasks that they have been used for, from low-level vision
(feature detection and tracking, optic flow, etc.) to high-level vision
(reconstruction, segmentation, recognition). We also discuss the techniques
developed to process events, including learning-based techniques, as well as
specialized processors for these novel sensors, such as spiking neural
networks. Additionally, we highlight the challenges that remain to be tackled
and the opportunities that lie ahead in the search for a more efficient,
bio-inspired way for machines to perceive and interact with the world
The state-of-the-art progress in cloud detection, identification, and tracking approaches: a systematic review
A cloud is a mass of water vapor floating in the atmosphere. It is visible from the ground and can remain at a variable height for some time. Clouds are very important because their interaction with the rest of the atmosphere has a decisive influence on weather, for instance by sunlight occlusion or by bringing rain. Weather denotes atmosphere behavior and is determinant in several human activities, such as agriculture or energy capture. Therefore, cloud detection is an important process about which several methods have been investigated and published in the literature. The aim of this paper is to review some of such proposals and the papers that have been analyzed and discussed can be, in general, classified into three types. The first one is devoted to the analysis and explanation of clouds and their types, and about existing imaging systems. Regarding cloud detection, dealt with in a second part, diverse methods have been analyzed, i.e., those based on the analysis of satellite images and those based on the analysis of images from cameras located on Earth. The last part is devoted to cloud forecast and tracking. Cloud detection from both systems rely on thresholding techniques and a few machine-learning algorithms. To compute the cloud motion vectors for cloud tracking, correlation-based methods are commonly used. A few machine-learning methods are also available in the literature for cloud tracking, and have been discussed in this paper too
Application of advanced technology to space automation
Automated operations in space provide the key to optimized mission design and data acquisition at minimum cost for the future. The results of this study strongly accentuate this statement and should provide further incentive for immediate development of specific automtion technology as defined herein. Essential automation technology requirements were identified for future programs. The study was undertaken to address the future role of automation in the space program, the potential benefits to be derived, and the technology efforts that should be directed toward obtaining these benefits
Radar-based Application of Pedestrian and Cyclist Micro-Doppler Signatures for Automotive Safety Systems
Die sensorbasierte Erfassung des Nahfeldes im Kontext des hochautomatisierten Fahrens erfährt einen spürbaren 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 ungeschützte Verkehrsteilnehmer wie Fußgänger und Radfahrer mittels eines hochauflösenden Automotive-Radars analysiert. Dabei steht die Erscheinung des Mikro-Doppler-Effekts, hervorgerufen durch das hohe Maß 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 Bewegungszuständen gesetzt werden. Es werden neuartige Methoden vorgestellt, die die geometrischen und kinematischen Ausdehnungen der Objekte berücksichtigen und echtzeitfähige Ansätze zur Klassifikation und Verhaltensindikation realisieren.
Wird ein ausgedehntes Ziel (z.B. Radfahrer) von einem Radarsensor detektiert, können aus dessen Mikro-Doppler-Signatur wesentliche Eigenschaften bezüglich seines Bewegungszustandes innerhalb eines Messzyklus erfasst werden. Die Geschwindigkeitsverteilungen der sich drehenden Räder erlauben eine adaptive Eingrenzung der Tretbewegung, deren Verhalten essentielle Merkmale im Hinblick auf eine vorausschauende Unfallprädiktion aufweist. Ferner unterliegen ausgedehnte Radarziele einer Orientierungsabhängigkeit, 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 orientierungsabhängigen 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 Fußgängers dessen Beinbewegungen über die Zeit schätzt (Tracking) und wertvolle Objektinformationen hinsichtlich seines Bewegungsverhaltens offenbart. Dazu wird ein Bewegungsmodell entwickelt, das die nichtlineare Fortbewegung des Beins approximiert und dessen hohes Maß an biomechanischer Variabilität abbildet. Durch die Einbeziehung einer wahrscheinlichkeitsbasierten Datenassoziation werden die Radar-Detektionen ihren jeweils hervorrufenden Quellen (linkes und rechtes Bein) zugeordnet und
eine Trennung der Gliedmaßen 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 für zukünftige 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 objects’ 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 state’s fundamental properties can be captured from its micro-Doppler signature within a measurement
cycle. The spinning wheels’ 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 target’s 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 pedestrian’s leg motion over time (tracking) based on the pedestrian’s micro-Doppler profile and reveals valuable
object information regarding his motion behavior. To this end, a motion model is developed that approximates the leg’s 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 information’s 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
Pattern Recognition
A wealth of advanced pattern recognition algorithms are emerging from the interdiscipline between technologies of effective visual features and the human-brain cognition process. Effective visual features are made possible through the rapid developments in appropriate sensor equipments, novel filter designs, and viable information processing architectures. While the understanding of human-brain cognition process broadens the way in which the computer can perform pattern recognition tasks. The present book is intended to collect representative researches around the globe focusing on low-level vision, filter design, features and image descriptors, data mining and analysis, and biologically inspired algorithms. The 27 chapters coved in this book disclose recent advances and new ideas in promoting the techniques, technology and applications of pattern recognition
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