Analysis and Improvements to the Green Falcon Solar Powered UAV

There has recently been a rapidly increasing interest in solar powered UAVs. With the emergence of high power density batteries, long range and low-power micro radio devices, airframes, and powerful micro-processors and motors, small/micro UAVs have become applicable in civilian applications such as remote sensing, mapping, traffic monitoring, search and rescue. The Green Falcon UAV is an innovative project from Queensland University of Technology and has been developed and tested during these past years. It comprises a wide range of subsystems to be analyses and studied such as Solar Panel Cells, Gas sensor, Aerodynamics of the wing and others. Previous test however, resulted in damage to the solar cells and some of the subsystems including motor and ESC. This report describes the repair and verification process followed to improve the efficiency of the Green Falcon UAV. The report shows some of the results obtained in previous static and flight tests as well as some of recommendations.

Datu-trafikoaren monitorizazio-sistemetarako aplikatutako ereduen ebaluazio-erreminta baten diseinua eta garapena

[EU]Lan honetan, software diseinu bat sortu nahi da, zeinaren bidez datu-trafikoen monitorizazio sistemak aztertzeko garatu den eredu matematiko bat ebaluatuko den. Eredu horrentzat interfaze bat egin beharko da, eta interfaze horrek esandako eredua ebaluatzeko softwareaz gain, software gehiago biltzeko ahalmena eduki beharko du. Horrela, ikertzaileek Trafikoa Monitorizatzeko Sistemak aztertzeko sortzen diren eredu matematikoak sistema bakarra erabiliz ebaluatu ahalko dute ahalik eta modu errazenean.

Interfaz Gráfica para la Automatización y Gestión de Escenarios Experimentales (IGAGEE).

[ES]El sistema IGAGEE proporciona una solución integrada para facilitar la automatización de pruebas y la gestión de equipamiento y resultados en entornos experimentales de laboratorio. En este tipo de escenarios es necesario controlar elementos diversos de forma coordinada y manejar información compleja de configuración y resultados. El sistema IGAGEE permite: (a) resolver de forma adecuada la integración de las herramientas disponibles, (b) solucionar la organización de las configuraciones y resultados, (c) ofrecer una interfaz única a los usuarios para la gestión y visualización de datos y (d) resolver la gestión remota del equipamiento de pruebas. Se ha diseñado el sistema IGAGEE buscando soluciones generales aplicables a entornos diferentes. Para la validación de las propuestas de diseño se ha desarrollado un prototipo inicial atendiendo a las necesidades concretas del grupo de investigación NQaS en su línea de investigación sobre Análisis y Monitorización de Tráfico en red.

交通监控系统中运动目标分类和跟踪研究

文章讲述了交通监控系统中应用视频图像流来跟踪运动目标并对目标进行分类的具体过程和原则.基于目标检测提出了双差分的目标检测算法,目标分类应用到了连续时间限制和最大可能性估计的原则,目标跟踪则结合检测到的运动目标图像和当前模板进行相关匹配.实验结果表明,该过程能够很好地探测和分类目标,去除背景信息的干扰,并能够在运动目标部分被遮挡、外观改变和运动停止等情况下连续地跟踪目标.

Robust Sketching and Aggregation of Distributed Data Streams

The data streaming model provides an attractive framework for one-pass summarization of massive data sets at a single observation point. However, in an environment where multiple data streams arrive at a set of distributed observation points, sketches must be computed remotely and then must be aggregated through a hierarchy before queries may be conducted. As a result, many sketch-based methods for the single stream case do not apply directly, as either the error introduced becomes large, or because the methods assume that the streams are non-overlapping. These limitations hinder the application of these techniques to practical problems in network traffic monitoring and aggregation in sensor networks. To address this, we develop a general framework for evaluating and enabling robust computation of duplicate-sensitive aggregate functions (e.g., SUM and QUANTILE), over data produced by distributed sources. We instantiate our approach by augmenting the Count-Min and Quantile-Digest sketches to apply in this distributed setting, and analyze their performance. We conclude with experimental evaluation to validate our analysis.

Genetic algorithm for shipping route estimation with long-range tracking data : automatic reconstruction of shipping routes based on the historical ship positions for maritime safety applications

Ship tracking systems allow Maritime Organizations that are concerned with the Safety at Sea to obtain information on the current location and route of merchant vessels. Thanks to Space technology in recent years the geographical coverage of the ship tracking platforms has increased significantly, from radar based near-shore traffic monitoring towards a worldwide picture of the maritime traffic situation. The long-range tracking systems currently in operations allow the storage of ship position data over many years: a valuable source of knowledge about the shipping routes between different ocean regions. The outcome of this Master project is a software prototype for the estimation of the most operated shipping route between any two geographical locations. The analysis is based on the historical ship positions acquired with long-range tracking systems. The proposed approach makes use of a Genetic Algorithm applied on a training set of relevant ship positions extracted from the long-term storage tracking database of the European Maritime Safety Agency (EMSA). The analysis of some representative shipping routes is presented and the quality of the results and their operational applications are assessed by a Maritime Safety expert.

A Trainable Object Detection System: Car Detection in Static Images

This paper describes a general, trainable architecture for object detection that has previously been applied to face and peoplesdetection with a new application to car detection in static images. Our technique is a learning based approach that uses a set of labeled training data from which an implicit model of an object class -- here, cars -- is learned. Instead of pixel representations that may be noisy and therefore not provide a compact representation for learning, our training images are transformed from pixel space to that of Haar wavelets that respond to local, oriented, multiscale intensity differences. These feature vectors are then used to train a support vector machine classifier. The detection of cars in images is an important step in applications such as traffic monitoring, driver assistance systems, and surveillance, among others. We show several examples of car detection on out-of-sample images and show an ROC curve that highlights the performance of our system.

A novel application of a microaccelerometer for target classification

This paper presents a novel method of target classification by means of a microaccelerometer. Its principle is that the seismic signals from moving vehicle targets are detected by a microaccelerometer, and targets are automatically recognized by the advanced signal processing method. The detection system based on the microaccelerometer is small in size, light in weight, has low power consumption and low cost, and can work under severe circumstances for many different applications, such as battlefield surveillance, traffic monitoring, etc. In order to extract features of seismic signals stimulated by different vehicle targets and to recognize targets, seismic properties of typical vehicle targets are researched in this paper. A technique of artificial neural networks (ANNs) is applied to the recognition of seismic signals for vehicle targets. An improved back propagation (BP) algorithm and ANN architecture have been presented to improve learning speed and avoid local minimum points in error curve. The improved BP algorithm has been used for classification and recognition of seismic signals of vehicle targets in the outdoor environment. Through experiments, it can be proven that target seismic properties acquired are correct, ANN is effective to solve the problem of classification and recognition of moving vehicle targets, and the microaccelerometer can be used in vehicle target recognition.

Efficient road detection and tracking for unmanned aerial vehicle

Abstract - An unmanned aerial vehicle (UAV) has many applications in a variety of fields. Detection and tracking of a specific road in UAV videos play an important role in automatic UAV navigation, traffic monitoring, and ground–vehicle tracking, and also is very helpful for constructing road networks for modeling and simulation. In this paper, an efficient road detection and tracking framework in UAV videos is proposed. In particular, a graph-cut–based detection approach is given to accurately extract a specified road region during the initialization stage and in the middle of tracking process, and a fast homography-based road-tracking scheme is developed to automatically track road areas. The high efficiency of our framework is attributed to two aspects: the road detection is performed only when it is necessary and most work in locating the road is rapidly done via very fast homography-based tracking. Experiments are conducted on UAV videos of real road scenes we captured and downloaded from the Internet. The promising results indicate the effectiveness of our proposed framework, with the precision of 98.4% and processing 34 frames per second for 1046 x 595 videos on average.

Verkehrsmonitoring mit Hilfe von Radarfernerkundung

Für die Zukunft wird eine Zunahme an Verkehr prognostiziert, gleichzeitig herrscht ein Mangel an Raum und finanziellen Mitteln, um weitere Straßen zu bauen. Daher müssen die vorhandenen Kapazitäten durch eine bessere Verkehrssteuerung sinnvoller genutzt werden, z.B. durch Verkehrsleitsysteme. Dafür werden räumlich aufgelöste, d.h. den Verkehr in seiner flächenhaften Verteilung wiedergebende Daten benötigt, die jedoch fehlen. Bisher konnten Verkehrsdaten nur dort erhoben werden, wo sich örtlich feste Meßeinrichtungen befinden, jedoch können damit die fehlenden Daten nicht erhoben werden. Mit Fernerkundungssystemen ergibt sich die Möglichkeit, diese Daten flächendeckend mit einem Blick von oben zu erfassen. Nach jahrzehntelangen Erfahrungen mit Fernerkundungsmethoden zur Erfassung und Untersuchung der verschiedensten Phänomene auf der Erdoberfläche wird nun diese Methodik im Rahmen eines Pilotprojektes auf den Themenbereich Verkehr angewendet. Seit Ende der 1990er Jahre wurde mit flugzeuggetragenen optischen und Infrarot-Aufnahmesystemen Verkehr beobachtet. Doch bei schlechten Wetterbedingungen und insbesondere bei Bewölkung, sind keine brauchbaren Aufnahmen möglich. Mit einem abbildenden Radarverfahren werden Daten unabhängig von Wetter- und Tageslichtbedingungen oder Bewölkung erhoben. Im Rahmen dieser Arbeit wird untersucht, inwieweit mit Hilfe von flugzeuggetragenem synthetischem Apertur Radar (SAR) Verkehrsdaten aufgenommen, verarbeitet und sinnvoll angewendet werden können. Nicht nur wird die neue Technik der Along-Track Interferometrie (ATI) und die Prozessierung und Verarbeitung der aufgenommenen Verkehrsdaten ausführlich dargelegt, es wird darüberhinaus ein mit dieser Methodik erstellter Datensatz mit einer Verkehrssimulation verglichen und bewertet. Abschließend wird ein Ausblick auf zukünftige Entwicklungen der Radarfernerkundung zur Verkehrsdatenerfassung gegeben.

Desarrollo de Técnicas Interferométricas para Radares de Alta Resolución en Milimétricas

En esta tesis doctoral se aborda el desarrollo de técnicas interferométricas para radares de alta resolución en milimétricas. Específicamente, se centra en el desarrollo de técnicas para radares terrestres y en concreto aquellas relacionadas con el seguimiento, clasificación y generación de imágenes de blancos móviles. Aparte del desarrollo teórico y simulación de las diferentes técnicas, uno de los grandes retos de esta tesis es la verificación experimental de las mismas. Para este propósito, se empleará un prototipo de sensor radar interferométrico de muy alta resolución y de ondas milimétricas. El primer capítulo de la tesis realiza una pequeña introducción a las técnicas interferométricas y presenta los objetivos, motivación y organización del presente trabajo. El segundo capítulo hace una pequeña introducción a los radares interferométricos de alta resolución en milimétricas y presenta el sensor radar experimental con el que se llevarán a cabo la validación de las distintas técnicas presentadas. El tercer capítulo recoge las distintas técnicas interferométricas desarrolladas para el seguimiento y cálculo de la altura de blancos móviles en radares de alta resolución. Entre ellas se encuentran: el seguimiento de blancos móviles en sucesión de imágenes ISAR, el cálculo de altura de blancos en imágenes radar bidimensionales distancia-tiempo y su equivalente en imágenes distancia-Doppler. El cuarto capítulo presenta una aplicación de las técnicas interferométricas para la vigilancia de tráfico en carreteras. Se describirán dos configuraciones radar que permiten calcular la velocidad de todos los blancos iluminados por el radar identificando de manera unívoca a los blancos en función del carril por el que circulen. El quinto capítulo presenta técnicas interferométricas aplicadas a la vigilancia en entornos marítimos basadas en la generación de imágenes interferométricas. Para demostrar la viabilidad del uso de estas imágenes se ha desarrollado un simulador de blancos móviles extensos realistas. Abstract This Ph. D thesis deals with the development of radar interferometry techniques for high-resolution millimeter wave sensors. It focuses on the development of techniques for ground-based radars and specifically those related to monitoring, classification and imaging of moving targets. Apart from the theoretical development and simulation, another major technical challenge of this thesis is the experimental verification of the different techniques. For that purpuse, a very high resolution interferometric millimeter wave radar sensor is used. The first chapter of the thesis makes a brief introduction to the interferometric techniques and shows the goals, motivation and organization of this work. The second chapter provides a brief introduction to high resolution interferometric radars in millimeter waves and presents the experimental radar sensor which will be used for the validation of the various techniques presented. The third chapter presents the different interferometric techniques developed for monitoring and obtaining the height of moving targets in high resolution radars. Among them are: tracking of moving targets in a succession of ISAR images, targets height calculation using bidimensional range-time radar images and the equivalente technique using range-Doppler images. The fourth chapter presents the application of interferometric techniques for road traffic monitoring. Two radar configurations are described. Both of them are able to obtain the speed for simultaneuslly illuminated targets, and univocally identify each target based on the detected road lane. The fifth chapter presents the application of interferometric techniques to maritime surveillance based on interferometric imaging. To demonstrate the feasibility of the presented techniques a realistic simulator of extended moving targets has been developed.

Multisensor data fusion for accurate modelling of mobile objects

In the last decade, multi-sensor data fusion has become a broadly demanded discipline to achieve advanced solutions that can be applied in many real world situations, either civil or military. In Defence,accurate detection of all target objects is fundamental to maintaining situational awareness, to locating threats in the battlefield and to identifying and protecting strategically own forces. Civil applications, such as traffic monitoring, have similar requirements in terms of object detection and reliable identification of incidents in order to ensure safety of road users. Thanks to the appropriate data fusion technique, we can give these systems the power to exploit automatically all relevant information from multiple sources to face for instance mission needs or assess daily supervision operations. This paper focuses on its application to active vehicle monitoring in a particular area of high density traffic, and how it is redirecting the research activities being carried out in the computer vision, signal processing and machine learning fields for improving the effectiveness of detection and tracking in ground surveillance scenarios in general. Specifically, our system proposes fusion of data at a feature level which is extracted from a video camera and a laser scanner. In addition, a stochastic-based tracking which introduces some particle filters into the model to deal with uncertainty due to occlusions and improve the previous detection output is presented in this paper. It has been shown that this computer vision tracker contributes to detect objects even under poor visual information. Finally, in the same way that humans are able to analyze both temporal and spatial relations among items in the scene to associate them a meaning, once the targets objects have been correctly detected and tracked, it is desired that machines can provide a trustworthy description of what is happening in the scene under surveillance. Accomplishing so ambitious task requires a machine learning-based hierarchic architecture able to extract and analyse behaviours at different abstraction levels. A real experimental testbed has been implemented for the evaluation of the proposed modular system. Such scenario is a closed circuit where real traffic situations can be simulated. First results have shown the strength of the proposed system.

A study of feature combination for vehicle detection based on image processing

Video analytics play a critical role in most recent traffic monitoring and driver assistance systems. In this context, the correct detection and classification of surrounding vehicles through image analysis has been the focus of extensive research in the last years. Most of the pieces of work reported for image-based vehicle verification make use of supervised classification approaches and resort to techniques, such as histograms of oriented gradients (HOG), principal component analysis (PCA), and Gabor filters, among others. Unfortunately, existing approaches are lacking in two respects: first, comparison between methods using a common body of work has not been addressed; second, no study of the combination potentiality of popular features for vehicle classification has been reported. In this study the performance of the different techniques is first reviewed and compared using a common public database. Then, the combination capabilities of these techniques are explored and a methodology is presented for the fusion of classifiers built upon them, taking into account also the vehicle pose. The study unveils the limitations of single-feature based classification and makes clear that fusion of classifiers is highly beneficial for vehicle verification.