Modern cars are often equipped with sensors like radar, infrared cameras and stereo cameras that collect information about its surroundings. By using a stereo camera, it is possible to receive information about the distance to points in front of the car. This information can be used to estimate the height of the predicted path of the car. An application which does this is the stereo based Road surface preview (RSP) algorithm. By using the output from the RSP algorithm it is possible to use active suspension control, which controls the vertical movement of the wheels relative to the chassis. This application primarily makes the driving experience more comfortable, but also extends the durability of the vehicle. The idea behind this Master’s thesis is to create an evaluation tool for the RSP algorithm, which can be used at arbitrary roads.  The thesis describes the proposed evaluation tool, where focus has been to make an accurate comparison of camera data received from the RSP algorithm and laser data used as ground truth in this thesis. Since the tool shall be used at the company proposing this thesis, focus has also been on making the tool user friendly. The report discusses the proposed methods, possible sources to errors and improvements. The evaluation tool considered in this thesis shows good results for the available test data, which made it possible to include an investigation of a possible improvement of the RSP algorithm.

Depth estimation using stereo images is an important task in many computer vision applications. A stereo camera contains two image sensors that observe the scene from slightly different viewpoints, making it possible to find the depth of the scene. An active stereo camera also uses a laser projector that projects a pattern into the scene. The advantage of the laser pattern is the additional texture that gives better depth estimations in dark and textureless areas.  Recently, deep learning methods have provided new solutions producing state-of-the-art performance in stereo reconstruction. The aim of this project was to investigate the behavior of a deep learning model for active stereo reconstruction, when using data from different cameras. The model is self-supervised, which solves the problem of having enough ground truth data for training the model. It instead uses the known relationship between the left and right images to let the model learn the best estimation. The model was separately trained on datasets from three different active stereo cameras. The three trained models were then compared using evaluation images from all three cameras. The results showed that the model did not always perform better on images from the camera that was used for collecting the training data. However, when comparing the results of different models using the same test images, the model that was trained on images from the camera used for testing gave better results in most cases.

The purpose of this study was to evaluate how the BS.1770 Loudness Standard is affected by the amount of difference signal present in the signal being measured, and if this affection is different between the two playback systems Headphones and Loudspeakers. The study was restricted to rock music productions in a stereo format. The results obtained from the study might provide useful information to mixing and mastering engineers, as it evaluates the correlation between spatial information and subjective loudness. The study consisted of an active listening test, containing six stimuli with different Sum and Difference Ratio (SDR). The test was done in both headphones and loudspeakers, and the difference in volume as set by the subjects were noted. The results from the headphone version and the loudspeaker version were then compared in a paired t-test to see if there was a significant difference between the two formats. The results pointed to the factors of Playback System and SDR to have non- significant effect on the results. After analyzing the possible error sources, it became apparent that other factors had a far greater effect on the results. The results imply that the BS.1770 Loudness Standard can accurately measure the loudness of a given stereo rock music material, despite the fact that it does not consider the differences between the channels when conducting the measurement. Whether or not the effect being studied is significant in other conditions is not verified, due to the restrictions of the study. Further studies would be needed in order to verify the findings of this study, preferably with more attention to detail since there were apparent flaws in the method used.

Detecção de pedestres é uma importante área em visão computacional com o potencial de salvar vidas quando aplicada em veículos. Porém, essa aplicação exige detecções em tempo real, com alta acurácia e menor quantidade de falsos positivos possível. Durante os últimos anos, diversas ideias foram exploradas e os métodos mais recentes que utilizam arquiteturas profundas de redes neurais possibilitaram um grande avanço nesta área, melhorando significativamente o desempenho das detecções. Apesar desse progresso, a detecção de pedestres que estão distantes do veículo continua sendo um grande desafio devido às suas pequenas escalas na imagem, sendo necessária a avaliação da eficácia dos métodos atuais em evitar ou atenuar a gravidade dos acidentes de trânsito que envolvam pedestres. Dessa forma, como primeira proposta deste trabalho, foi realizado um estudo para avaliar a aplicabilidade dos métodos estado-da-arte para evitar colisões em cenários urbanos. Para isso, a velocidade e dinâmica do veículo, o tempo de reação e desempenho dos métodos de detecção foram considerados. Através do estudo, observou-se que em ambientes de tráfego rápido ainda não é possível utilizar métodos visuais de detecção de pedestres para assistir o motorista, pois nenhum deles é capaz de detectar pedestres que estão distantes do veículo e, ao mesmo tempo, operar em tempo real. Mas, ao considerar apenas pedestres em maiores escalas, os métodos tradicionais baseados em janelas deslizantes já conseguem atingir um bom desempenho e rápida execução. Dessa forma, com a finalidade de restringir a operação dos detectores apenas para pedestres em maiores escalas e assim, possibilitar a aplicação de métodos visuais em veículos, foi proposta uma configuração de câmeras que possibilitou obter imagens para um maior intervalo de distância à frente do veículo com pedestres em resolução quase duas vezes maior em comparação à uma câmera comercial. Resultados experimentais mostraram considerável melhora no desempenho das detecções, possibilitando superar a dificuldade causada pelas pequenas escalas dos pedestres nas imagens. / Pedestrian detection is an important area in computer vision with the potential to save lives when applied on vehicles. This application requires accurate detections and real-time operation, keeping the number of false positives as minimal as possible. Over the past few years, several ideas were explored, including approaches with deep network architectures, which have reached considerably better performances. However, detecting pedestrians far from the camera is still challenging due to their small sizes on images, making it necessary to evaluate the effectiveness of existing approaches on avoiding or reducing traffic accidents that involves pedestrians. Thus, as the first proposal of this work, a study was done to verify the state-of-the-art methods applicability for collision avoidance in urban scenarios. For this, the speed and dynamics of the vehicle, the reaction time and performance of the detection methods were considered. The results from this study show that it is still not possible to use a vision-based pedestrian detector for driver assistance on urban roads with fast moving traffic, since none of them is able to handle real-time pedestrian detection. However, for large-scale pedestrians on images, methods based on sliding window approach can already perform reliably well with fast inference time. Thus, in order to restrict the operation of detectors only for pedestrians in larger scales and enable the application of vision-based methods in vehicles, it was proposed a camera setup that provided to get images for a larger range of distances in front of the vehicle with pedestrians resolution almost twice as large compared to a commercial camera. Experimental results reveal a considerable enhancement on detection performance, overcoming the difficulty caused by the reduced scales that far pedestrians have on images.

Este trabalho apresentou uma análise comparativa de algumas técnicas de correlações locais baseadas em intensidade luminosa, as quais são: Soma das Diferenças Absolutas, Soma dos Quadrados das Diferenças, Correlação Cruzada Normalizada, Transformada Rank e Transformada Censo. Para as comparações foram adotadas imagens estéreos disponíveis em repositórios de universidades e suas variantes com a inclusão de ruído e variação de intensidade luminosa. Após a implementação dos algoritmos escolhidos e a comparação de seus resultados, foi obtido que a Transformada Censo é um dos métodos com os piores resultados apresentando grande quantidade de correlações erradas. Foram apresentadas modificações para melhorar a performance desse método e os resultados obtidos foram melhores. / This work presents a comparative analysis of some local area intensity based correlation algorithm, which are: Sum of Absolute Differences, Sum of Squared Differences, Normalized Cross-Correlation, Rank Transform and Census Transform. For the tests stereo data sets are adopted. These data sets are available at universities websites and their variants with the inclusion of noise and variation of luminosity are created. After implementing the chosen algorithms a comparison were performed and the Census Transform was one of the methods that got the worst results showing large quantity of false correlations. On this work was presented some modifications to improve the performance of the Census Transform and the results obtained were better than the original Census Transform.

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De acordo com a Organização Mundial da Saúde,cerca de 1,2milhões de pessoas no mundo morrem em acidentes de trânsito. Sistemas de assistência ao motorista e veículos autônomos podem diminuir o número de acidentes. Dentre as várias demandas existentes para viabilizar essa tecnologia, sistemas computacionais de percepção ainda permanecem sem uma solução definitiva. Dois deles, detecção de obstáculos e de via navegável, normalmente fazem uso de algoritmos sofisticados como técnicas de aprendizado supervisionado, que mostram resultados impressionantes quando treinados com bases de dados bem definidas e diversificadas.Entretanto, construir, manter e atualizar uma base de dados com exemplos de vários lugares do mundo e em diversas situações é trabalhoso e complexo. Assim, métodos adaptativos e auto-supervisionados mostram-se como boas alternativas para sistemas de detecção do futuro próximo. Neste contexto, esta tese apresenta um método para estimar obstáculose via navegável através de sensores de baixo custo (câmeras estereoscópicas), sem o uso de técnicas de aprendizado de máquina e de diversas suposições normalmente utilizadas por trabalhos já disponíveis na literatura. Esses métodos utilizando sensor estereoscópico foram comparados fazendo uso de sensores do tipo 3D-LIDAR e mostraram resultados semelhantes. Este sistema poderá ser usado como uma fase pré-processamento de dados para melhorar ou viabilizar métodos adaptativos de aprendizado. / World wide, an estimated 1.2million lives are lostin road crashes each year and Advanced Driver Assistance Systems (ADAS) and Self-driving cars promise to reduce this number. Among the various issues to complete this technology, perception systems are still an unsolved issues. Normally two of them, obstacle detection and road detection, make use of sophisticated algorithms such as supervised machine learning methods which can perform with impressive results if it was trained with good data sets. Since it is a complex and an expensive job to create and maintain data bases of scenarios from the entire world, adaptive and/or self-supervised methods are good candidates for detection systems in the near future. Due that, this thesis present a method to estimate obsta- cles and estimate the road terrain using low cost sensors (stereo camera), avoiding supervised machine learning techniques and the most common assumptions used by works presented in literature. These methods were compared with 3D-LIDAR approaches achieving similar results and thus it can be used as a pre-processing step to improve or allow adaptive methods with machine learning systems.

O trabalho apresentado por essa dissertação busca contribuir com a atenuação do problema da correspondência em visão estéreo a partir de uma abordagem local de soluções. São usadas duas estratégias como solução às ambigüidades e às oclusões da cena: a análise multi-resolução das imagens empregando a estrutura piramidal, e a força de agrupamento perceptual, conhecida como Gestalt theory na psicologia. Inspirado no sistema visual humano, a visão estéreo é uma área de grande interesse em visão computacional, e está relacionada à recuperação de informações tridimensionais de uma cena a partir de imagens da mesma. Para isso, as imagens são capturadas em posições diferentes para o futuro relacionamento das várias projeções de um mesmo ponto 3D. Apesar de ser estudada há quase quatro décadas, ela ainda apresenta problemas de difícil solução devido às dificuldades relacionadas às distorções produzidas pela mudança da perspectiva de visualização. Dentre esses problemas destacam-se os relacionados à oclusão de pontos e também à ambigüidade gerada pela repetição ou ausência de textura nas imagens. Esses por sua vez compõem a base do problema estéreo, chamado de problema da correspondência. Os resultados obtidos são equivalentes aos obtidos por técnicas globais, com a vantagem de requerer menor complexidade computacional. O uso da teoria de agrupamento perceptual faz desse trabalho um método moderno de estimação de disparidades, visto que essa técnica é alvo de atenção especial em recentes estudos na área de visão computacional. / This work aims to give a contribution to the correspondence problem using a local approach. Two strategies are used as solution to ambiguities and occlusions: the multi-resolution analysis with irnages pyramids and the other is the perceptual grouping weight, called Gestalt theory in the psychology. Inspired by human vision system, the stereo vision is an very important area in computer vision. It is related with the 3D information recovery from a pair of images. The images are captured frorn different positions to hereafter association of the 3D point projections. Although it has being studied for quite a long time, stereo vision presents some difficult problems, related to the change of visualisation perspective. Among the different problems originated from point of view changes, occlusions and ambiguities have special attention and compose the foundation of stereo problem, named correspondence problem. The results obtained were closer to the ones generated by global techniques, with the advantage of requiring less computational complexity. The use of Gestalt theory makes this a modern disparity estimation method, as this theory has been received special attention in computer vision researchs.

Research efforts on the development of autonomous vehicles date back to the 1920s and recent announcements indicate that those cars are close to becoming commercially available. However, the most successful prototypes that are currently being demonstrated rely on an expensive set of sensors. This study investigates the use of an affordable vision system as a planner for the Robocart, an autonomous golf cart prototype developed by the Wireless Innovation Laboratory at WPI. The proposed approach relies on a stereo vision system composed of a pair of Raspberry Pi computers, each one equipped with a Camera Module. They are connected to a server and their clocks are synchronized using the Precision Time Protocol (PTP). The server uses timestamps to obtain a pair of simultaneously captured images. Images are processed to generate a disparity map using stereo matching and points in this map are reprojected to the 3D world as a point cloud. Then, an occupancy grid is built and used as input for an A* graph search that finds a collision-free path for the robot. Due to the non-holonomic constraints of a car-like robot, a Pure Pursuit algorithm is used as the control method to guide the robot along the computed path. The cameras are also used by a Visual Odometry algorithm that tracks points on a sequence of images to estimate the position and orientation of the vehicle. The algorithms were implemented using the C++ language and the open source library OpenCV. Tests in a controlled environment show promising results and the interfaces between the server and the Robocart have been defined, so that the proposed method can be used on the golf cart as soon as the mechanical systems are fully functional.

Pedestrian detection is a canonical instance of object detection that remains a popular topic of research and a key problem in computer vision due to its diverse applications. These applications have the potential to positively improve the quality of life. In recent years, the number of approaches to detecting pedestrians in monocular and binocular images has grown steadily. However, the use of multispectral imaging is still uncommon. This thesis work presents a novel approach to data and feature fusion of a multispectral imaging system for pedestrian detection. It also includes the design and building of a test rig which allows for quick data collection of real-world driving. An application of the mathematical theory of trifocal tensor is used to post process this data. This allows for pixel level data fusion across a multispectral set of data. Performance results based on commonly used SVM classification architectures are evaluated against the collected data set. Lastly, a novel cascaded SVM architecture used in both classification and detection is discussed. Performance improvements through the use of feature fusion is demonstrated.