People Matching for Transportation Planning Using Texel Camera Data for Sequential Estimation

This paper addresses automatic people matching in the dynamic setting of public transportation, such as a bus, as people enter and then at some later time exit from a doorway. Matching a person entering to the same person exiting at a later time provides accurate information about individual riders, such as how long a person is on a bus and the associated stops the person uses. At a higher level, matching exits to previous entry events provides information about the distribution of traffic flow across the whole transportation system. The proposed techniques may be applied at any gateway where the flow of human traffic is to be analyzed. For the purpose of associating entry and exit events, a trellis optimization algorithm is used for sequence estimation, based on multiple texel camera measurements. Since the number of states in the trellis exponentially grows with the number of persons currently on the bus, a beam search pruning technique is employed to manage the computational and memory load. Experimental results using real texel camera measurements show 96% matching accuracy for 68 people exiting a bus in a randomized order. In a bus route simulation where a true traffic flow distribution is used to randomly draw entry and exit events for simulated riders, the proposed sequence estimation algorithm produces an estimated traffic flow distribution, which provides an excellent match to the true distribution

People Matching for Transportation Planning Using Optimized Features and Texel Camera Data for Sequential Estimation

This thesis explores pattern recognition in the dynamic setting of public transportation, such as a bus, as people enter and later exit from a doorway. Matching the entrance and exit of each individual provides accurate information about individual riders such as how long a person is on a bus and which stops the person uses. At a higher level, matching exits to entries provides information about the distribution of traffic flow across the whole transportation system. A texel camera is implemented and multiple measures of people are made where the depth and color data are generated. A large number of features are generated and the sequential floating forward selection (SFFS) algorithm is used for selecting the optimized features. Criterion functions using marginal accuracy and maximization of minimum normalized Mahalanobis distance are designed and compared. Because of the particular case of the bus environment, which is a sequential estimation problem, a trellis optimization algorithm is designed based on a sequence of measurements from the texel camera. Since the number of states in the trellis grows exponentially with the number of people currently on the bus, a beam search pruning technique is employed to manage the computational and memory load. Experimental results using real texel camera measurements show good results for 68 people exiting from an initially full bus in a randomized order. In a bus route simulation where a true traffic flow distribution is used to randomly draw entry and exit events for simulated riders, the proposed sequential estimation algorithm produces an estimated traffic flow distribution which provides an excellent match to the true distribution

Gaze-Based Human-Robot Interaction by the Brunswick Model

We present a new paradigm for human-robot interaction based on social signal processing, and in particular on the Brunswick model. Originally, the Brunswick model copes with face-to-face dyadic interaction, assuming that the interactants are communicating through a continuous exchange of non verbal social signals, in addition to the spoken messages. Social signals have to be interpreted, thanks to a proper recognition phase that considers visual and audio information. The Brunswick model allows to quantitatively evaluate the quality of the interaction using statistical tools which measure how effective is the recognition phase. In this paper we cast this theory when one of the interactants is a robot; in this case, the recognition phase performed by the robot and the human have to be revised w.r.t. the original model. The model is applied to Berrick, a recent open-source low-cost robotic head platform, where the gazing is the social signal to be considered

Improved terrain type classification using UAV downwash dynamic texture effect

The ability to autonomously navigate in an unknown, dynamic environment, while at the same time classifying various terrain types, are significant challenges still faced by the computer vision research community. Addressing these problems is of great interest for the development of collaborative autonomous navigation robots. For example, an Unmanned Aerial Vehicle (UAV) can be used to determine a path, while an Unmanned Surface Vehicle (USV) follows that path to reach the target destination. For the UAV to be able to determine if a path is valid or not, it must be able to identify the type of terrain it is flying over. With the help of its rotor air flow (known as downwash e↵ect), it becomes possible to extract advanced texture features, used for terrain type classification. This dissertation presents a complete analysis on the extraction of static and dynamic texture features, proposing various algorithms and analyzing their pros and cons. A UAV equipped with a single RGB camera was used to capture images and a Multilayer Neural Network was used for the automatic classification of water and non-water-type terrains by means of the downwash e↵ect created by the UAV rotors. The terrain type classification results are then merged into a georeferenced dynamic map, where it is possible to distinguish between water and non-water areas in real time. To improve the algorithms’ processing time, several sequential processes were con verted into parallel processes and executed in the UAV onboard GPU with the CUDA framework achieving speedups up to 10x. A comparison between the processing time of these two processing modes, sequential in the CPU and parallel in the GPU, is also presented in this dissertation. All the algorithms were developed using open-source libraries, and were analyzed and validated both via simulation and real environments. To evaluate the robustness of the proposed algorithms, the studied terrains were tested with and without the presence of the downwash e↵ect. It was concluded that the classifier could be improved by per forming combinations between static and dynamic features, achieving an accuracy higher than 99% in the classification of water and non-water terrain.Dotar equipamentos moveis da funcionalidade de navegação autónoma em ambientes desconhecidos e dinâmicos, ao mesmo tempo que, classificam terrenos do tipo água e não água, são desafios que se colocam atualmente a investigadores na área da visão computacional. As soluções para estes problemas são de grande interesse para a navegação autónoma e a colaboração entre robôs. Por exemplo, um veículo aéreo não tripulado (UAV) pode ser usado para determinar o caminho que um veículo terrestre não tripulado (USV) deve percorrer para alcançar o destino pretendido. Para o UAV conseguir determinar se o caminho é válido ou não, tem de ser capaz de identificar qual o tipo de terreno que está a sobrevoar. Com a ajuda do fluxo de ar gerado pelos motores (conhecido como efeito downwash), é possível extrair características de textura avançadas, que serão usadas para a classificação do tipo de terreno. Esta dissertação apresenta uma análise completa sobre extração de texturas estáticas e dinâmicas, propondo diversos algoritmos e analisando os seus prós e contras. Um UAV equipado com uma única câmera RGB foi usado para capturar as imagens. Para classi ficar automaticamente terrenos do tipo água e não água foi usada uma rede neuronal multicamada e recorreu-se ao efeito de downwash criado pelos motores do UAV. Os re sultados da classificação do tipo de terreno são depois colocados num mapa dinâmico georreferenciado, onde é possível distinguir, em tempo real, terrenos do tipo água e não água. De forma a melhorar o tempo de processamento dos algoritmos desenvolvidos, vários processos sequenciais foram convertidos em processos paralelos e executados na GPU a bordo do UAV, com a ajuda da framework CUDA, tornando o algoritmo até 10x mais rápido. Também são apresentadas nesta dissertação comparações entre o tempo de processamento destes dois modos de processamento, sequencial na CPU e paralelo na GPU. Todos os algoritmos foram desenvolvidos através de bibliotecas open-source, e foram analisados e validados, tanto através de ambientes de simulação como em ambientes reais. Para avaliar a robustez dos algoritmos propostos, os terrenos estudados foram testados com e sem a presença do efeito downwash. Concluiu-se que o classificador pode ser melhorado realizando combinações entre as características de textura estáticas e dinâmicas, alcançando uma precisão superior a 99% na classificação de terrenos do tipo água e não água

CT Scanning

Since its introduction in 1972, X-ray computed tomography (CT) has evolved into an essential diagnostic imaging tool for a continually increasing variety of clinical applications. The goal of this book was not simply to summarize currently available CT imaging techniques but also to provide clinical perspectives, advances in hybrid technologies, new applications other than medicine and an outlook on future developments. Major experts in this growing field contributed to this book, which is geared to radiologists, orthopedic surgeons, engineers, and clinical and basic researchers. We believe that CT scanning is an effective and essential tools in treatment planning, basic understanding of physiology, and and tackling the ever-increasing challenge of diagnosis in our society

Recreation studied from above : airphoto interpretation as input into land evaluation for recreation

Recreation and tourism are of growing importance not only in the industrialized part of the world, but also In developing countries. Remote sensing and in particular airphoto interpretation can be used in several ways as input into land evaluation for recreation and tourism. An inventory of recreational facilities that can give a first indication of the spatial pattern of recreation, can be partly done by airphoto interpretation. For the analysis of the relation between recreational facilities and their resources, remote sensing can give the best overview of the landscape. The use of sequences of airphotos can reveal development processes. An extreme stage of recreational impact, recreational erosion, can be identified on airphotos. The gradual process underlying it can best be assessed from sequences of airphotos of different years. Airphotos are a useful tool too for the analysis of the spatial behaviour of recreationists, and sequences taken during one season or one day can provide the time dimension to that behaviour.Airphotos are also ideal for analysis of the visual structure of the landscape, a first step to landscape evaluation.Satellite images are not yet suitable for most applications, but in a number of studies reasonable results have been obtained with SPOT images, for example for change analysis and for the analysis of the visual structure of the landscape.Application of remote sensing is especially appropriate in situations where conventional sources of data are scarce, completely lacking, obsolete or incomplete

Моделі, алгоритми та програмне забезпечення для планування шляху для навігації мобільних роботів з уникненням перешкод на основі дерева октантів

Об'єкт дослідження: процес оптимізації та покращення точності руху та уникнення перешкод для навігації мобільних роботів. Предмет дослідження: моделі та методи виявлення перешкод та навігації з метою уникнення виявлених перешкод. Мета магістерської роботи: підвищення ефективності системи розпізнавання перешкод мобільними роботами для навігації у середовищі, використовуючи датчики для забезпечення дороги без зіткнень з об’єктами, які не знаходяться на одному рівні з лазерами. Методи дослідження. Для вирішення поставлених задач використані методи: пошуку шляхів, порогового значення, обробки хмари точок, генерації дерева октантів. Наукова новизна полягає у тому, що удосконалено методи системи планування шляху для навігації мобільних роботів на основі дерева октантів для якісного та точного шляху від початкової точки до заданої у просторі

Virtual Reality Games for Motor Rehabilitation

This paper presents a fuzzy logic based method to track user satisfaction without the need for devices to monitor users physiological conditions. User satisfaction is the key to any product’s acceptance; computer applications and video games provide a unique opportunity to provide a tailored environment for each user to better suit their needs. We have implemented a non-adaptive fuzzy logic model of emotion, based on the emotional component of the Fuzzy Logic Adaptive Model of Emotion (FLAME) proposed by El-Nasr, to estimate player emotion in UnrealTournament 2004. In this paper we describe the implementation of this system and present the results of one of several play tests. Our research contradicts the current literature that suggests physiological measurements are needed. We show that it is possible to use a software only method to estimate user emotion