Face Detection of Thermal Images in Various Standing Body-Pose using Facial Geometry

 Automatic face detection in frontal view for thermal images is a primary task in a health system e.g. febrile identification or security system e.g. intruder recognition. In a daily state, the scanned person does not always stay in frontal face view. This paper develops an algorithm to identify a frontal face in various standing body-pose. The algorithm used an image processing method where first it segmented face based on human skin’s temperature. Some exposed non-face body parts could also get included in the segmentation result, hence discriminant features of a face were applied. The shape features were based on the characteristic of a frontal face, which are: (1) Size of a face, (2) facial Golden Ratio, and (3) Shape of a face is oval. The algorithm was tested on various standing body-pose that rotate 360° towards 2 meters and 4 meters camera-to-object distance. The accuracy of the algorithm on face detection in a manageable environment is 95.8%. It detected face whether the person was wearing glasses or not

Real-time classification of vehicle types within infra-red imagery.

Real-time classification of vehicles into sub-category types poses a significant challenge within infra-red imagery due to the high levels of intra-class variation in thermal vehicle signatures caused by aspects of design, current operating duration and ambient thermal conditions. Despite these challenges, infra-red sensing offers significant generalized target object detection advantages in terms of all-weather operation and invariance to visual camouflage techniques. This work investigates the accuracy of a number of real-time object classification approaches for this task within the wider context of an existing initial object detection and tracking framework. Specifically we evaluate the use of traditional feature-driven bag of visual words and histogram of oriented gradient classification approaches against modern convolutional neural network architectures. Furthermore, we use classical photogrammetry, within the context of current target detection and classification techniques, as a means of approximating 3D target position within the scene based on this vehicle type classification. Based on photogrammetric estimation of target position, we then illustrate the use of regular Kalman filter based tracking operating on actual 3D vehicle trajectories. Results are presented using a conventional thermal-band infra-red (IR) sensor arrangement where targets are tracked over a range of evaluation scenarios

Deteção de pessoas para Smart Autonomous Mobile Units

Dissertação de mestrado integrado em Engenharia Eletrónica Industrial e de ComputadoresO interesse por veículos autónomos tem aumentado nos últimos tempos. São veículos dotados com alguma inteligência que lhes permite decidir sempre qual o percurso a tomar para atingir o alvo, sem requerer ajuda de operadores ou de uma marcação explícita do caminho a seguir. Para terem sucesso nestas tarefas, estes veículos devem possuir sistemas de perceção do ambiente circundante que sejam robustos e precisos, de modo a otimizar as rotas e a evitar potenciais obstáculos que se encontrem ao seu redor. O caso de estudo para o tema desta dissertação é a perceção do ambiente em redor de um veículo autónomo de movimentação interna de materiais num chão de fábrica. Esta tarefa é parte integrante do projeto da IFactory que resulta de uma parceria entre a Universidade do Minho e a Bosh car Multimedia Portugal, S.A.. O principal objetivo é a aplicação deste veículo numa zona de produção da Bosh Car Multimédia Portugal, S.A.. Neste tipo de ambientes industriais existem inúmeros obstáculos que se podem opor à rota do veículo. Para além de ser necessário a deteção dos mesmos é ainda requerida a sua identificação, pois mediante o tipo de obstáculo o comportamento a exibir pelo veículo poderá ter de ser diferente. São várias as tecnologias que têm vindo a ser desenvolvidas que podem ser usadas neste tipo de aplicações. Apesar das inúmeras vantagens que cada uma das tecnologias possui, estas mesmas por si só não são suficientes para garantir segurança e robustez, sendo então necessário usar várias em simultâneo e recorrer a métodos de fusão sensorial. Outro fator também muito importante são os algoritmos e os métodos a utilizar. Estes vão permitir a análise ou o tratamento da informação obtida dos sensores, para serem retiradas as devidas informações. Para a comunicação entre os vários algoritmos e os sensores do veículo recorreu-se ao middleware ROS, fornecendo este bibliotecas e ferramentas de suporte de modo a simplificar todo o desenvolvimento de software. O sistema de perceção proposto nesta dissertação foca-se na deteção de pessoas. Este recorre a sensores LiDAR 2D para a deteção de um padrão de pernas e a uma câmara 3D para a deteção da parte superior do corpo humano. Neste documento, é realizada toda a implementação dos algoritmos assim como a descrição do funcionamento. Por fim são apresentados todos os resultados e conclusões dos métodos utilizados.In the last years the interest for autonomous vehicles has increased. They are vehicles with some intelligence that allows them to always decide which route to take to reach the target without requiring the help of operators or any explicit marking on the way to follow. In order to be successful in these tasks, these vehicles must have environment perception systems that are robust and accurate, so that their navigation system always choose the best route and avoid potential obstacles that can be found around them. The case study for the topic of this dissertation is the environment perception around an autonomous vehicle for material transport in a factory floor. This task is an integral part of the IFactory project that results from a partnership between the University of Minho and Bosh car Multimedia Portugal, S.A.. The main objective is to apply this vehicle in a production area of Bosh car Multimedia Portugal, S.A.. In this type of environment there are numerous obstacles that can oppose the route of the vehicle. In addition to the need of detecting them, it is still required to identify them, because the behavior of the vehicle may change, depending on the type of obstacle. There are several technologies that can be used in this type of applications. Despite the many advantages that each technology has, these alone are not enough to guarantee security and robustness, so it is necessary to use several technologies simultaneously and use sensor fusion methods. Other important factors are the algorithms and methods to implement. They will allow obtaining all sensor data and perform all the analysis and processing required for that information. The communication between the algorithms and the sensors of the vehicle is done by the middleware ROS. This middleware provides libraries and support tools that will allow simplifying all the software development. The perception system proposed in this dissertation focused on people detection. This uses 2D LiDAR sensors for the detection of a leg pattern and a 3D camera for the detection of the human upper body. All the implementation of the algorithms, as well as the description of the operation are carried out in this document. Finally, all the results and conclusions for the methods used are presented

Improving Quantitative Infrared Imaging for Medical Diagnostic Applications

Infrared (IR) thermography is a non-ionizing and non-invasive imaging modality that allows the measurement of the spatial and temporal variations of the infrared radiation emitted by the human body. The emitted radiation and the skin surface temperature that can be derived from the emitted radiation data carry a wealth of information about different processes within the human body. To advance the quantitative use of IR thermography in medical diagnostics, this dissertation investigates several issues critical to the demands imposed by clinical applications. We developed a computational thermal model of the human skin with multiple layers and a near-surface lesion to understand the thermal behavior of skin tissue in dynamic infrared imaging. With the aid of this model, various cooling methods and conditions suitable for the clinical application of dynamic IR imaging are critically evaluated. The analysis of skin cooling provides a quantitative basis for the selection and optimization of cooling conditions in the clinical practice of dynamic IR imaging. To improve the quantitative accuracy for the analysis of dynamic IR imaging, we proposed a motion tracking approach using a template-based algorithm. The motion tracking approach is capable of following the involuntary motion of the subject in the IR image sequence, thereby allowing us to track the temperature evolution for a particular region on the skin. In addition, to compensate for the measurement artifacts induced by the surface curvature in IR thermography, a correction formula was developed based on the emissivity model and phantom experiments. The correction formula was integrated into a 3D imaging procedure based on a system combining Kinect and IR cameras. We demonstrated the feasibility of mapping 2D IR images onto the 3D surface of the human body. The accuracy of temperature measurement was improved by applying the correction method. Finally, we designed a variety of quantitative approaches to analyze the clinical data acquired from patient studies of pigmented lesions and hemangiomas. These approaches allow us to evaluate the thermal signatures of lesions with different characteristics, measured in both static and dynamic IR imaging. The collection of methodologies described in this dissertation, leading to improved ease of use and accuracy, can contribute to the broader implementation of quantitative IR thermography in medical diagnostics