Lane Formation Beyond Intuition Towards an Automated Characterization of Lanes in Counter-flows

Pedestrian behavioural dynamics have been growingly investigated by means of (semi)automated computing techniques for almost two decades, exploiting advancements on computing power, sensor accuracy and availability, computer vision algorithms. This has led to a unique consensus on the existence of significant difference between unidirectional and bidirectional flows of pedestrians, where the phenomenon of lane formation seems to play a major role. The collective behaviour of lane formation emerges in condition of variable density and due to a self-organisation dynamic, for which pedestrians are induced to walk following preceding persons to avoid and minimize conflictual situations. Although the formation of lanes is a well-known phenomenon in this field of study, there is still a lack of methods offering the possibility to provide an (even semi-) automatic identification and a quantitative characterization. In this context, the paper proposes an unsupervised learning approach for an automatic detection of lanes in multi-directional pedestrian flows, based on the DBSCAN clustering algorithm. The reliability of the approach is evaluated through an inter-rater agreement test between the results achieved by a human coder and by the algorithm

VisuaLeague III: Visual Analytics of Multiple Games

Tese de mestrado, Informática, Universidade de Lisboa, Faculdade de Ciências, 2020Digital data available has been growing over the last years and with it, the need to create representative ways to understand and make use of its potential with visualization techniques that can be applied in different purposes. One of these cases are eSports (electronic sports), considered nowadays a sport with high growth expectation, and for which data analyses can have a significant impact. One of the most popular game type practiced in eSports is the Multiplayer Online Battle Arena (MOBA) genre represented by one of the most popular competitive games, League of Legends (LoL), which will be the case study for this thesis. As many traditional sports, there are various events to have in consideration when observing performance of gameplay. In addition to statistics for each game there is relevant information on players’ positions (spatial data), in a specific period in time (temporal data). Specific events in a game, related with objectives, can also be considered, such as purchasing an item, player kills, destroying towers, or complete objectives. Having a way to analyze and visualize this data helps not only programmers and game designers to improve gameplay but also players, coaches and analysts to improve player performance. The objective of this work is to redesign the previous prototype VisuaLeague II, and propose a new version, VisuaLeague III in order to explore techniques to implement analysis for multiple games, team searches and access to professional games’ training sections, scrims. Common problems presented in the analysis with voluminous amount of data, like cluttering and overlapping, are addressed by adding filters to searches, interaction with the visualizations, aggregation of data, and clustering. The developed prototype, VisuaLeague III was evaluated by professional coaches to understand if the searches and visualization techniques implemented are adequate for analysing players’ performance in a competitive environment. The results demonstrate overall positive attitude with particular interest in analysis for custom games and multiple games analysis as those provide visualizations that do not exist in common tools, specially, regarding spatiotemporal data

A Unified And Green Platform For Smartphone Sensing

Smartphones have become key communication and entertainment devices in people\u27s daily life. Sensors on (or attached to) smartphones can enable attractive sensing applications in different domains, including environmental monitoring, social networking, healthcare, transportation, etc. Most existing smartphone sensing systems are application-specific. How to leverage smartphones\u27 sensing capability to make them become unified information providers for various applications has not yet been fully explored. This dissertation presents a unified and green platform for smartphone sensing, which has the following desirable features: 1) It can support various smartphone sensing applications; 2) It is personalizable; 2) It is energy-efficient; and 3) It can be easily extended to support new sensors. Two novel sensing applications are built and integrated into this unified platform: SOR and LIPS. SOR is a smartphone Sensing based Objective Ranking (SOR) system. Different from a few subjective online review and recommendation systems (such as Yelp and TripAdvisor), SOR ranks a target place based on data collected via smartphone sensing. LIPS is a system that learns the LIfestyles of mobile users via smartPhone Sensing (LIPS). Combining both unsupervised and supervised learning, a hybrid scheme is proposed to characterize lifestyle and predict future activities of mobile users. This dissertation also studies how to use the cloud as a coordinator to assist smartphones for sensing collaboratively with the objective of reducing sensing energy consumption. A novel probabilistic model is built to address the GPS-less energy-efficient crowd sensing problem. Provably-good approximation algorithms are presented to enable smartphones to sense collaboratively without accurate locations such that sensing coverage requirements can be met with limited energy consumption

Development of a Cost-Efficient Multi-Target Classification System Based on FMCW Radar for Security Gate Monitoring

Radar systems have a long history. Like many other great inventions, the origin of radar systems lies in warfare. Only in the last decade, radar systems have found widespread civil use in industrial measurement scenarios and automotive safety applications. Due to their resilience against harsh environments, they are used instead of or in addition to optical or ultrasonic systems. Radar sensors hold excellent capabilities to estimate distance and motion accurately, penetrate non-metallic objects, and remain unaffected by weather conditions. These capabilities make these devices extremely flexible in their applications. Electromagnetic waves centered at frequencies around 24 GHz offer high precision target measurements, compact antenna, and circuitry design, and lower atmospheric absorption than higher frequency-based systems. This thesis studies non-cooperative automatic radar multi-target detection and classification. A prototype of a radar system with a new microwave-radar-based technique for short-range detection and classification of multiple human and vehicle targets passing through a road gate is presented. It allows identifying different types of targets, i.e., pedestrians, motorcycles, cars, and trucks. The developed system is based on a low-cost 24 GHz off-the-shelf FMCW radar, combined with an embedded Raspberry Pi PC for data acquisition and transmission to a remote processing PC, which takes care of detection and classification. This approach, which can find applications in both security and infrastructure surveillance, relies upon the processing of the scattered-field data acquired by the radar. The developed method is based on an ad-hoc processing chain to accomplish the automatic target recognition task, which consists of blocks performing clutter and leakage removal with a frame subtraction technique, clustering with a DBSCAN approach, tracking algorithm based on the \u3b1-\u3b2 filter to follow the targets during traversal, features extraction, and finally classification of targets with a classification scheme based on support vector machines. The approach is validated in real experimental scenarios, showing its capabilities incorrectly detecting multiple targets belonging to different classes (i.e., pedestrians, cars, motorcycles, and trucks). The approach has been validated with experimental data acquired in different scenarios, showing good identification capabilities

Seamless Interactions Between Humans and Mobility Systems

As mobility systems, including vehicles and roadside infrastructure, enter a period of rapid and profound change, it is important to enhance interactions between people and mobility systems. Seamless human—mobility system interactions can promote widespread deployment of engaging applications, which are crucial for driving safety and efficiency. The ever-increasing penetration rate of ubiquitous computing devices, such as smartphones and wearable devices, can facilitate realization of this goal. Although researchers and developers have attempted to adapt ubiquitous sensors for mobility applications (e.g., navigation apps), these solutions often suffer from limited usability and can be risk-prone. The root causes of these limitations include the low sensing modality and limited computational power available in ubiquitous computing devices. We address these challenges by developing and demonstrating that novel sensing techniques and machine learning can be applied to extract essential, safety-critical information from drivers natural driving behavior, even actions as subtle as steering maneuvers (e.g., left-/righthand turns and lane changes). We first show how ubiquitous sensors can be used to detect steering maneuvers regardless of disturbances to sensing devices. Next, by focusing on turning maneuvers, we characterize drivers driving patterns using a quantifiable metric. Then, we demonstrate how microscopic analyses of crowdsourced ubiquitous sensory data can be used to infer critical macroscopic contextual information, such as risks present at road intersections. Finally, we use ubiquitous sensors to profile a driver’s behavioral patterns on a large scale; such sensors are found to be essential to the analysis and improvement of drivers driving behavior.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163127/1/chendy_1.pd

Ohjelmointitehtävien klusterointi tarkistuksen ja tutkimisen tehostamiseksi

Programming courses often receive large quantities of program code submissions to exercises which, due to their large number, are graded and students provided feedback automatically. Teachers might never review these submissions therefore losing a valuable source of insight into student programming patterns. This thesis researches how these submissions could be reviewed efficiently using a software system, and a prototype, CodeClusters, was developed as an additional contribution of this thesis. CodeClusters' design goals are to allow the exploration of the submissions and specifically finding higher-level patterns that could be used to provide feedback to students. Its main features are full-text search and n-grams similarity detection model that can be used to cluster the submissions. Design science research is applied to evaluate CodeClusters' design and to guide the next iteration of the artifact and qualitative analysis, namely thematic synthesis, to evaluate the problem context as well as the ideas of using software for reviewing and providing clustered feedback. The used study method was interviews conducted with teachers who had experience teaching programming courses. Teachers were intrigued by the ability to review submitted student code and to provide more tailored feedback to students. The system, while still a prototype, is considered worthwhile to experiment on programming courses. A tool for analyzing and exploring submissions seems important to enable teachers to better understand how students have solved the exercises. Providing additional feedback can be beneficial to students, yet the feedback should be valuable and the students incentivized to read it

REMOTE SENSING METHODS FOR THE INVESTIGATION OF THE EVOLUTION AND DYNAMICS OF ALPINE LANDSCAPES

Whilst the effects of present-day climate change are apparent in many environmental systems, much less is known about its impact upon the geomorphic systems characteristic of Alpine environments. This is an important knowledge gap because of the potential vulnerability of Alpine landscapes. The gap exists for two primary reasons: (1) observing climate forcing is challenging because it is manifest over timescales of decades to centuries, over which timescale geomorphic data are commonly scarce; and (2) the geomorphic response of landscapes to climate change can be complex, reflecting both spatially differential sensitivities to climate forcing and the effects of landscape heritage associated with historical glacial activity. Nonetheless, there is a consensus in the scientific community about the potentially high sensitivity of Alpine regions to climate change, because of the vulnerability of permafrost, glacial and nival processes to changes in atmospheric temperature and precipitation and the large amount of sediment stored on the associated hillsides. One approach to addressing this knowledge gap is to harness the power of remote sensing. A number of active and passive remote sensing methods could be employed for the reconstruction and monitoring of both whole landscapes and individual landforms. This Thesis aims to use such approaches to quantify the geomorphic dynamics of high mountain areas at the timescale of decades and so in the context of recent and rapid climate warming. It does so recognizing that both endogenous (landscape legacy) and exogenous (climatic forcing) processes may matter. To support this primary aim, a secondary aim arises: the evaluation of the potential of a number of remote sensing techniques for landscape and landform monitoring at multiple temporal and spatial scales. Thus this Thesis also tests in an Alpine setting the geomorphological potential of photogrammetric methods, using both aerial and hand-held sensors and both traditional and the innovative Structure-from-Motion processing approaches, and Terrestrial Laser Scanner techniques. The Thesis shows that remote sensing approaches prove to be an advantageous approach for a number of scales of application. In particular, over large spatial extents and in the case of decadal scale climate forcing of Alpine landscapes, photogrammetry was found to be capable of quantifying process rates within the limits of detection determined by the resolution of historical imagery. The information unlocked from aerial archives reveals distinct geomorphic responses to cold and warm periods and to changes in rates of precipitation and snow cover. Nonetheless, whilst enhanced sediment production is observed locally, evidence suggest a weak propagation of climate change signals through the landscape due to impeded connection to the river system and/or sediment transport capacity limitation. -- Bien que les effets des changements climatiques actuels soient visibles dans de nombreux systèmes environnementaux, un manque de connaissances des impacts sur les paysages alpins persiste. Cette lacune existe pour deux raisons principales : (1) l'observation du forçage climatique représente un défi, car ses conséquences se manifestent sur des périodes de plusieurs décennies, voire des siècles, pour lesquels les données géomorphologiques sont généralement rares ; et (2) la réaction du paysage aux changements climatiques peut être complexe, reflétant à la fois des sensibilités différentes au niveau spatial et les effets du patrimoine paysager, comme par exemple son histoire glaciaire. Néanmoins, il existe un consensus dans la communauté scientifique à propos de la haute sensibilité potentielle des régions alpines au changement climatique, en raison de la vulnérabilité du pergélisol et des processus glaciaires et neigeux aux changements de température atmosphérique et des précipitations et en raison de la grande quantité de sédiments stockés sur les versants alpins. Une stratégie pour aborder ces problématiques s'appuie sur le potentiel de la télédétection. Une série de méthodes de télédétection active et passive peuvent être utilisées pour reconstruire et surveiller le paysage entier et les éléments individuels qui le composent. Cette thèse vise l'application de ces approches pour quantifier les dynamiques géomorphologiques des paysages de haute montagne à l'échelle des décennies, et donc dans le contexte du réchauffement climatique récent et actuel. Cela est mis en pratique par la reconnaissance de l'importance des processus endogènes (héritage du paysage) et exogènes (forçage climatique). Le soutien à cet objectif en soulève un deuxième : l'évaluation du potentiel d'un certain nombre de techniques de télédétection pour le monitorage du relief et de ses formes géomorphologiques à plusieurs échelles temporelles et spatiales. Ainsi, cette thèse teste le potentiel des méthodes de photogrammétrie, en utilisant à la fois des senseurs aéroportés et portatifs et des approches de traitements traditionnels et innovants, et du balayage laser terrestre pour la recherche géomorphologique alpine. Les résultats obtenus montrent que les approches de télédétection se révèlent avantageuses pour des nombreuses échelles d'application. En particulier, sur de grandes étendues spatiales et dans le contexte du forçage climatique du paysage alpin, la photogrammétrie aérienne d'archive se montre appropriée pour la quantification des taux des processus dans les limites de détection déterminées par la résolution des photographies historiques. Les résultats démontrent l'existence d'une réponse géomorphologique distincte pour des périodes froides ou chaudes, ainsi que selon les variations des taux de précipitations et de couverture de neige. Néanmoins, alors qu'une production accrue de sédiments est observée localement, des évidences suggèrent une faible propagation des signaux du changement climatique à travers le paysage. Les raisons semblent être une faible contribution des versants au réseau fluvial et/ou une capacité de transport des sédiments limitée. -- Obwohl die Auswirkungen des aktuellen Klimawandels in zahlreichen Umweltsystemen beobachtet wurden, sind die Kenntnisse dieser Auswirkungen auf alpine Landschaften immer noch ungenügend. Diese Lücke existiert aus folgenden Gründen: (1) Das Beobachten klimatischer Auswirkungen auf alpine geomorphologische Prozesse stellt eine grosse Herausforderung dar, da diese sich über eine Zeitspanne von mehreren Jahrzehnten bis Jahrhunderten bemerkbar machen können, für die meist nur wenige geomorphologische Daten zur Verfügung stehen. (2) Durch die unterschiedlichen Empfind- lichkeiten verschiedener geomorphologischer Landschaftselemente sowie durch den grossen Einfluss des landschaftlichen Erbes, wie zum Beispiel der historischen Gletschertätigkeit, reagieren alpine Landschaftsentwicklungsprozesse sehr komplex auf Veränderungen des Klimas. Nichtsdestotrotz, auf- grund der hohen Empfindlichkeit des Permafrosts und der Gletscher- und Schneeprozesse gegenüber Veränderungen der atmosphärischen Temperatur und der Niederschlagsmenge sowie der grossen Menge an Sedimenten die an den Alpenhängen abgelagert werden und wurden, herrscht in der wis- senschaftlichen Gemeinschaft ein breiter Konsens über die potentielle hohe Sensibilität der alpinen geomorphologischen Systeme in Bezug auf den zu erwartenden Klimawandel. Fernerkundung bietet ein hohes Potential, um die geomorphologische Sensibilität zu erkunden. Aktive und passive Fernerkundungsmethoden können genutzt werden, um gesamte Landschaften sowie ihre einzelnen geomorphologischen Elemente historisch zu rekonstruieren und kontinuierlich zu überwachen. Die vorliegende Dissertation zielt auf die Anwendung dieser Ansätze, um die geomorpho- logische Dynamik der hochalpinen Landschaft über Jahrzehnte, und somit im Kontext der jüngsten Klimaerwärmung, zu quantifizieren. Der hier dargestellte Ansatz fokussiert vor allem auf die Bedeutung der endogenen (landschaftliches Erbe) und exogenen (klimatische Einflüsse) Prozesse. Die Umsetzung dieses primären Ziels zieht ein sekundäres Ziel mit sich: Die Bewertung des Potenzials einer Reihe von Fernerkundungsmethoden für das Monitoring von Landschaften und ihrer geomorphologischen For- men auf mehreren rüumlichen und zeitlichen Skalen. Damit wird das Potenzial photogrammetrischer Methoden, insbesondere luftgestützter und tragbarer Sensoren in Kombination mit traditionellen und innovativen "Structure-from-Motion" Ansätzen, sowohl auch terrestrischen Laserscanning Techniken für die alpine geomorphologische Forschung getestet. Die Ergebnisse zeigen, dass die hier dargestellten Fernerkundungsansätze für eine breite Reihe von Anwendungsskalen vorteilhaft sind. Die Archiv-Luftphotogrammmetrie ist besonders für die Quan- tifizierung der Auswirkungen des Klimawandels auf geomorphologische Prozesse in grossen Land- schaftsausschnitten geeignet. Die Auflösung der historischen Luftbilder bestimmt die Detektionsgrenze dieser Prozesse. Die aus den Luftarchiven ermittelten Informationen zeigen, dass kalte und warme Klimaphasen, sowie Variationen der Niederschlagsmenge und der Schneedeckenmächtigkeit unter- schiedliche Auswirkungen auf geomorphologische Prozesse haben. Obwohl ein lokaler Anstieg der Sedimentproduktion beobachtet werden konnte, konnten nur geringe Anzeichen einer Ausbreitung dieser Klimawandelsignale in der Landschaft beobachtet werden. Die Gründe hierfür scheinen der geringe Beitrag der untersuchten Berghänge zum Gesamtwasserabfluss und/oder die beschränkte Sedimenttransportfähigkeit zu sein. -- Nonostante gli effetti del cambiamento climatico attuale siano evidenti in molti sistemi ambientali, una conoscenza deficitaria perdura riguardo il suo impatto sui paesaggi alpini. Tale lacuna esiste per due principali ragioni: (1) gli effetti del cambiamento climatico sono difficili da osservare, in quanto manifesti su scale temporali di decenni, o persino secoli, per le quali prevale una scarsità di dati geomorfologici esaustivi; e (2) la reazione del paesaggio a tali cambiamenti può essere complessa e riflettere al contempo delle sensibilità spaziali differenti e gli effetti del patrimonio paesaggistico, come ad esempio la cronistoria glaciale. Tuttavia, vi è un consenso nella comunità scientifica riguardo l'ele- vata sensibilità delle regioni alpine ai cambiamenti climatici, a causa della vulnerabilità di permafrost e processi glaciali e nevosi ai cambiamenti di temperatura atmosferica e di precipitazioni, oltre che all'ampio stoccaggio di sedimenti concentrato sui pendii alpini. Una strategia per colmare questa lacuna di conoscenza può essere l'avvalersi del potenziale delle tecniche di telerilevamento. Vari metodi di telerilevamento attivo e passivo possono essere impiegati per ricostruire e monitorare il paesaggio ed i singoli elementi che lo compongono. Questa tesi si propone di utilizzare tali metodi per quantificare le dinamiche geomorfologiche nelle regioni di alta montagna a scala temporale decennale, e quindi nel contesto del riscaldamento climatico recente e attuale. In tale approccio viene riconosciuta l'importanza dei processi di tipo endogeno (di eredità paesaggistica) ed exogeno (climatici). A sostegno di questo obiettivo primario, una seconda finalità si pone: lo sviluppo e la valutazione di diverse tecniche di telerilevamento per il monitoraggio dei rilievi alpini e delle loro forme geomorfologiche, a più scale temporali e spaziali. Pertanto, questa tesi mette alla prova metodi di fotogrammetria, utilizzando al contempo sensori aeroportati e portatili ed approcci tradizionali ed innovativi (come l'emergente Structure-from-Motion), e tecniche di scansione laser per la ricerca geomorfologica in scenari alpini. I risultati ottenuti dimostrano come gli approcci di telerilevamento rappresentino una risorsa efficace e vantaggiosa per una vasta gamma di applicazioni. In particolare, ad ampia scala spaziale e nel contesto di cambiamento climatico nelle regioni alpine, la fotogrammetria aerea d'archivio si è dimostrata appropriata per la quantificazione dei processi geomorfologici entro limiti di rilevamento determinati dalla risoluzione delle immagini storiche stesse. I risultati rivelano una reazione geomorfica distinta a periodi di caldo e freddo, oltre che a variazioni di precipitazioni e copertura nevosa. Ciononostante, malgrado un accrescimento della produzione sedimentaria sia presente a scala locale, la propagazione dei segnali di cambiamento climatico attraverso il paesaggio appare debole. La ragione risiede nello scarso contributo dei versanti al sistema fluviale e/o a limitate capacità di trasporto di sedimenti

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