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In this thesis, the structure from motion problem for calibrated scenes containing independently moving objects (IMO) has been studied. For this purpose, the overall reconstruction process is partitioned into various stages. The first stage deals with the fundamental problem of estimating structure and motion by using only two views. This process starts with finding some salient features using a sub-pixel version of the Harris corner detector. The features are matched by the help of a similarity and neighborhood-based matcher. In order to reject the outliers and estimate the fundamental matrix of the two images, a robust estimation is performed via RANSAC and normalized 8-point algorithms. Two-view reconstruction is finalized by decomposing the fundamental matrix and estimating the 3D-point locations as a result of triangulation. The second stage of the reconstruction is the generalization of the two-view algorithm for the N-view case. This goal is accomplished by first reconstructing an initial framework from the first stage and then relating the additional views by finding correspondences between the new view and already reconstructed views. In this way, 3D-2D projection pairs are determined and the projection matrix of this new view is estimated by using a robust procedure. The final section deals with scenes containing IMOs. In order to reject the correspondences due to moving objects, parallax-based rigidity constraint is used. In utilizing this constraint, an automatic background pixel selection algorithm is developed and an IMO rejection algorithm is also proposed. The results of the proposed algorithm are compared against that of a robust outlier rejection algorithm and found to be quite promising in terms of execution time vs. reconstruction quality.

Com o aumento na disponibilidade e considerável redução de custo das tecnologias que permitem a obtenção de dados de objetos móveis, torna-se abundante a oferta de dados de trajetórias. O estudo dessas trajetórias tem por objetivo permitir uma melhor compreensão dos dados, bem como a extração de novo conhecimento utilizando técnicas computacionais. Um tipo de estudo que pode ser realizado é a análise do comportamento das trajetórias de objetos móveis. O foco do estudo de padrões comportamentais em trajetórias tem sido a busca por padrões de aglomeração ou semelhança no deslocamento de entidades no espaço ou espaço-tempo. Este trabalho propõe a formalização de um novo padrão comportamental que indique quando um objeto móvel está evitando determinadas regiões espaciais. Este padrão foi denominado avoidance. A identificação e o estudo de tal comportamento pode ser de interesse de diversas áreas tais como segurança, jogos eletrônicos, comportamento social, entre outros. Inicialmente, este trabalho apresenta uma heurística com base nas observações das ocorrências do evento avoidance. A partir daí, são definidas as formalizações deste novo padrão. É apresentado, também, um algoritmo para identificar automaticamente o padrão comportamental avoidance em trajetórias de objetos móveis. Para avaliar a eficácia deste algoritmo, ao final desta dissertação são apresentados resultados de experimentos realizados em trajetórias coletadas por pedestres e por veículos em diversos locais da cidade de Porto Alegre e Xangri-lá. / With the increasing availability and considerable price reduction of technologies that allow the collection of moving object data, the offering of trajectory data becomes abundant. The study of these trajectories has the objective to allow a better understanding of the data, as well as the extraction of new knowledge using computational techniques. One of the possible studies is the analysis of the behavior of moving objects. The study on behavior patterns in trajectories has focused on agglomeration and similarity of entities moving in space or space and time. This work proposes the formalization of a new behavior pattern that indicates when a moving object is avoiding some determined spatial region, here called avoidance. The study and identification of this behavior may be of interest in many application areas, like security, electronic games, social behavior, and so on. Initially, this work presents an heuristic based on the observations of avoidance events, and based on this heuristic we define the formalization of this new kind of trajectory behavior pattern. This work also presents an algorithm to automatically identify this behavior in trajectories of moving objects. To evaluate the effectiveness of this algorithm, experiments were performed on trajectory datasets, collected by pedestrians in a park, and by vehicles at several places in the cities of Porto Alegre and Xangri-lá.

Com o aumento na disponibilidade e considerável redução de custo das tecnologias que permitem a obtenção de dados de objetos móveis, torna-se abundante a oferta de dados de trajetórias. O estudo dessas trajetórias tem por objetivo permitir uma melhor compreensão dos dados, bem como a extração de novo conhecimento utilizando técnicas computacionais. Um tipo de estudo que pode ser realizado é a análise do comportamento das trajetórias de objetos móveis. O foco do estudo de padrões comportamentais em trajetórias tem sido a busca por padrões de aglomeração ou semelhança no deslocamento de entidades no espaço ou espaço-tempo. Este trabalho propõe a formalização de um novo padrão comportamental que indique quando um objeto móvel está evitando determinadas regiões espaciais. Este padrão foi denominado avoidance. A identificação e o estudo de tal comportamento pode ser de interesse de diversas áreas tais como segurança, jogos eletrônicos, comportamento social, entre outros. Inicialmente, este trabalho apresenta uma heurística com base nas observações das ocorrências do evento avoidance. A partir daí, são definidas as formalizações deste novo padrão. É apresentado, também, um algoritmo para identificar automaticamente o padrão comportamental avoidance em trajetórias de objetos móveis. Para avaliar a eficácia deste algoritmo, ao final desta dissertação são apresentados resultados de experimentos realizados em trajetórias coletadas por pedestres e por veículos em diversos locais da cidade de Porto Alegre e Xangri-lá. / With the increasing availability and considerable price reduction of technologies that allow the collection of moving object data, the offering of trajectory data becomes abundant. The study of these trajectories has the objective to allow a better understanding of the data, as well as the extraction of new knowledge using computational techniques. One of the possible studies is the analysis of the behavior of moving objects. The study on behavior patterns in trajectories has focused on agglomeration and similarity of entities moving in space or space and time. This work proposes the formalization of a new behavior pattern that indicates when a moving object is avoiding some determined spatial region, here called avoidance. The study and identification of this behavior may be of interest in many application areas, like security, electronic games, social behavior, and so on. Initially, this work presents an heuristic based on the observations of avoidance events, and based on this heuristic we define the formalization of this new kind of trajectory behavior pattern. This work also presents an algorithm to automatically identify this behavior in trajectories of moving objects. To evaluate the effectiveness of this algorithm, experiments were performed on trajectory datasets, collected by pedestrians in a park, and by vehicles at several places in the cities of Porto Alegre and Xangri-lá.

The objective of this thesis is to present an algorithm for processing infrared images and accomplishing automatic detection and path tracking of moving subjects with fever. The detection is based on two main features: the distinction between the geometry of a human face and other objects in the field of view of the camera and the temperature of the radiating object. These features are used for tracking the identified person with fever. The position of camera with respect to direction of motion the walkers appeared to be critical in this process. Infrared thermography is a remote sensing technique used to measure temperatures based on emitted infrared radiation. This application may be used for fever screening in major public places such as airports and hospitals. For this study, we first look at human body and objects in a line of view with different temperatures that would be higher than the normal human body temperature (37.8C at morning and 38.3C at evening). As a part of the experimental study, two humans with different body temperatures walking a path were subjected to automatic fever detection applied for tracking the detected human with fever. The algorithm consists of image processing to threshold objects based on the temperature and template matching used for fever detection in a dynamic human environment.

Com o aumento na disponibilidade e considerável redução de custo das tecnologias que permitem a obtenção de dados de objetos móveis, torna-se abundante a oferta de dados de trajetórias. O estudo dessas trajetórias tem por objetivo permitir uma melhor compreensão dos dados, bem como a extração de novo conhecimento utilizando técnicas computacionais. Um tipo de estudo que pode ser realizado é a análise do comportamento das trajetórias de objetos móveis. O foco do estudo de padrões comportamentais em trajetórias tem sido a busca por padrões de aglomeração ou semelhança no deslocamento de entidades no espaço ou espaço-tempo. Este trabalho propõe a formalização de um novo padrão comportamental que indique quando um objeto móvel está evitando determinadas regiões espaciais. Este padrão foi denominado avoidance. A identificação e o estudo de tal comportamento pode ser de interesse de diversas áreas tais como segurança, jogos eletrônicos, comportamento social, entre outros. Inicialmente, este trabalho apresenta uma heurística com base nas observações das ocorrências do evento avoidance. A partir daí, são definidas as formalizações deste novo padrão. É apresentado, também, um algoritmo para identificar automaticamente o padrão comportamental avoidance em trajetórias de objetos móveis. Para avaliar a eficácia deste algoritmo, ao final desta dissertação são apresentados resultados de experimentos realizados em trajetórias coletadas por pedestres e por veículos em diversos locais da cidade de Porto Alegre e Xangri-lá. / With the increasing availability and considerable price reduction of technologies that allow the collection of moving object data, the offering of trajectory data becomes abundant. The study of these trajectories has the objective to allow a better understanding of the data, as well as the extraction of new knowledge using computational techniques. One of the possible studies is the analysis of the behavior of moving objects. The study on behavior patterns in trajectories has focused on agglomeration and similarity of entities moving in space or space and time. This work proposes the formalization of a new behavior pattern that indicates when a moving object is avoiding some determined spatial region, here called avoidance. The study and identification of this behavior may be of interest in many application areas, like security, electronic games, social behavior, and so on. Initially, this work presents an heuristic based on the observations of avoidance events, and based on this heuristic we define the formalization of this new kind of trajectory behavior pattern. This work also presents an algorithm to automatically identify this behavior in trajectories of moving objects. To evaluate the effectiveness of this algorithm, experiments were performed on trajectory datasets, collected by pedestrians in a park, and by vehicles at several places in the cities of Porto Alegre and Xangri-lá.

This master thesis has been done in the field of Advanced Driver Assistance Systems and presents a method to assist cross traffic at road junctions. An accurate tracking of crossing objects is necessary in order to assist traffic at road junctions. At Continental, the stable tracking of crossing objects is available, but the system still gives false alarms for non-colliding objects (e.g. Target Braking at crossroads). Hence the main focus of this thesis is on the reduction of false alarms for non colliding objects. Radar based Maneuver Detection function has been developed for Crossing Emergency Brake Assist system, which uses radar measurement parameters to detect the maneuvering of target objects in order to differentiate between collision and non-collision cases. Different crossing scenarios have been created in a Matlab environment and the algorithm is tested. Secondly, the algorithm is tested by using the measurement data from real recordings and evaluation is made. The proposed algorithm has reliably detected the non-collided objects (in normal cases) and helped in reducing the false alarm rate significantly.

"This thesis addresses important challenges in the areas of streaming and spatio-temporal databases. It focuses on continuous querying of spatio-temporal environments characterized by (1) a large number of moving and stationary objects and queries; (2) need for near real-time results; (3) limited memory and cpu resources; and (4) different accuracy requirements. The first part of the thesis studies the problem of performance vs. accuracy tradeoff using different location modelling techniques when processing continuous spatio-temporal range queries on moving objects. Two models for modeling the movement, namely: continuous and discrete models are described. This thesis introduces an accuracy comparison model to estimate the quality of the answers returned by each of the models. Experimental evaluations show the effectiveness of each model given certain characteristics of spatio-temporal environment (e.g., varying speed, location update frequency). The second part of the thesis introduces SCUBA, a Scalable Cluster Based Algorithm for evaluating a large set of continuous queries over spatio-temporal data streams. Unlike the commonly used static grid indices, the key idea of SCUBA is to group moving objects and queries based on common dynamic properties (e.g., speed, destination, and road network location) at run-time into moving clusters. This results in improvement in performance which facilitate scalability. SCUBA exploits shared cluster-based execution consisting of two phases. In phase I, the evaluation of a set of spatio-temporal queries is abstracted as a spatial join between moving clusters for cluster-based filtering of true negatives. There after, in phase II, a fine-grained join process is executed for all pairs identified as potentially joinable by a positive cluster-join match in phase I. If the clusters don’t satisfy the join predicate, the objects and queries that belong to those clusters can be savely discarded as being guaranteed to not join individually either. This provides processing cost savings. Another advantage of SCUBA is that moving cluster-driven load shedding is facilitated. A moving cluster (or its subset, called nucleus)approximates the locations of its members. As a consequence relatively accurate answers can be produced using solely the abstracted cluster location information in place of precise object-by-object matches, resulting in savings in memory and improvement in processing time. A theoretical analysis of SCUBA is presented with respect to the memory requirements, number of join comparisons and I/O costs. Experimental evaluations on real datasets demonstrate that SCUBA achieves a substantial improvement when executing continuous queries on highly dense moving objects. The experiments are conducted in a real data streaming system (CAPE) developed at WPI on real datasets generated by the Network-Based Moving Objects Generator."

La numérisation de nos espaces de vie et de mobilités s’est largement accentuée durant la dernière décennie. La multiplication des capteurs de toute nature permettant de percevoir et de mesurer notre espace physique en est le levier principal. L’ensemble de ces systèmes produit aujourd’hui de grands volumes de données hétérogènes sans cesse croissants, ce qui soulève de nombreux enjeux scientifiques et d'ingénierie en termes de stockage et de traitement pour la gestion et l’analyse de mobilités. Les travaux dans le domaine d’analyse des données spatio-temporelles ont largement été orientés soit vers la fouille de données historiques archivées, soit vers le traitement continu. Afin d’éviter les écueils de plus en plus prégnants dus à l’augmentation de ces volumes de données et de leur vélocité (temps de traitement trop long, modèles conceptuellement plus adaptés, analyse approximative des données), nous proposons la conception d’une approche hybride distribuée permettant le traitement combiné de flux temps-réel et de données archivées. L’objectif de cette thèse est donc de développer un nouveau système de gestion et de traitement distribué pour l’analyse des mobilités en particulier maritimes. La solution proposée répond principalement à des contraintes de temps-réel, les données archivées et les informations qui en sont extraites permettant d'améliorer la qualité de réponse. Une proposition de paradigme d'événements est également développée pour permettre ce traitement hybride mais aussi pour caractériser et identifier plus facilement des comportements types d'objets mobiles. Enfin, une requête appliquée sur des zones de couverture de signal pour des objets mobiles a été étudiée et testée sur des données maritimes mettant en exergue le besoin d'une approche hybride pour le traitement de trajectoires. / Over the past few years, the rapid prolifération of sensors and devices recording positioning information regularly produces very large volumes of heterogeneous data. This leads to many research challenges as the storage, distribution, management,Processing and analysis of the large mobility data generated still needs to be solved. Current works related to the manipulation of mobility data have been directed towards either mining archived historical data or continuous processing of incoming data streams.The aim of this research is to design a holistic System whose objective is to provide a combined processing of real time data streams and archived data positions. The proposed solution is real-time oriented, historical data and informations extracted from them allowing to enhance quality of the answers to queries. A event paradigm is discussed to facilitate the hybrid approach and to identify typical moving objects behaviors. Finally, a query concerning signal coverage of moving objects has been studied and applied to maritime data showing the relevance of a hybrid approach to deal with moving object data processing.

Σκοπός της παρούσας εργασίας είναι η παρουσίαση των σταδίων της πιο γενικής από τις παθητικές μεθόδους τρισδιάστατης ανακατασκευής στατικών σκηνών, της δομής από κίνηση. Εκτός της χρήσης στερεοσκοπικών ζευγών εικόνων για την δημιουργία πυκνών χαρτών βάθους, παρουσιάζεται και η περίπτωση χρήσης παράλληλων εικόνων, που αποτελούν την καλύτερη λύση σε περιπτώσεις που η κύρια συνιστώσα της κίνησης της κάμερας είναι παράλληλη στον οπτικό της άξονα. Επίσης, παρουσιάζεται μια πρωτότυπη γεωμετρική μέθοδος διόρθωσης των εικόνων τόσο για την δημιουργία στερεοσκοπικών ζευγών, όσο και για την περίπτωση των παράλληλων εικόνων. Σε κάθε στάδιο της όλης διαδικασίας, περιγράφονται οι μέθοδοι εκείνες που θεωρούνται οι αντιπροσωπευτικότερες της κατηγορίας τους. / The purpose of this paper is to present the phases of the most general passive method of three dimensional reconstruction of static scenes,called structure from motion.Besides using stereo images as the final result of image rectification; we examine the case of using parallel images that represent the best solution in cases where the main component of the camera motion is parallel to the optical axis. Also we show an original geometric correction method of images both for generating stereo pairs and parallel images pairs. At each stage of the process we describe some of the known methods that can be considered representative of their class.