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Many animal species, including many species of bats, exhibit collective behavior where groups of individuals coordinate their motion. Most bats are unique among these animals in that they use the active sensing mechanism of echolocation as their primary means of navigation. Due to their use of echolocation in large groups, bats run the risk of signal interference from sonar jamming. However, several species of bats have developed various strategies to prevent interference which may lead to different behavior when flying with conspecifics than when flying alone. This thesis seeks to explore the role of this sensing on the behavior of bat pairs flying together. Field data from a maternity colony of gray bats (Myotis grisescens) were collected using an array of cameras and microphones. These data were analyzed using the information theoretic measure of transfer entropy in order to quantify the interaction between pairs of bats and to determine the effect echolocation calls have on this interaction. Results show that there is evidence of information transfer in both the speed of the bats and their turning behavior, and that such evidence is absent when we consider their heading directions. Unidirectional information transfer was found in some subsets of the data which could be evidence of a leader-follower interaction. / Master of Science / Manyanimalspeciesexhibitcollectivebehaviorwheregroupsofanimalscoordinatetheir motion, as in flocking or schooling. Many species of bats also demonstrate this behavior. Bats are unique among these animals in that they use echolocation as their primary means of navigation. Bats produce ultrasonic pulses or calls and listen to the returning echo to "visualize" their environment. Bats using echolocation in large groups run the risk of other bat calls interfering with their ability to hear their own calls. They have developed various waystopreventinterferencewhichmayleadtodifferentbehaviorwhenflyingwithotherbats thanwhenflyingalone. Fielddatafromamaternitycolonyofgraybatswerecollectedusing a system of cameras and microphones. These data were analyzed to quantify the interaction between pairs of bats and to determine the effect echolocation calls have on this interaction. Results show that there is evidence of information transfer about both the speed of the bats and their turning behavior. There was also evidence of a possible leader-follower interaction in some subsets of the data.

The aim of this thesis is to provide methods for 2D segmentation and 2D/3D tracking, that are both fast and robust to imperfect image information, as caused for example by occlusions, motion blur and cluttered background. We do this by combining high level shape information with simultaneous segmentation and tracking. We base our work on the assumption that the space of possible 2D object shapes can be either generated by projecting down known rigid 3D shapes or learned from 2D shape examples. We minimise the discrimination between statistical foreground and background appearance models with respect to the parameters governing the shape generative process (the 6 degree-of-freedom 3D pose of the 3D shape or the parameters of the learned space). The foreground region is delineated by the zero level set of a signed distance function, and we define an energy over this region and its immediate background surroundings based on pixel-wise posterior membership probabilities. We obtain the differentials of this energy with respect to the parameters governing shape and conduct searches for the correct shape using standard non-linear minimisation techniques. This methodology first leads to a novel rigid 3D object tracker. For a known 3D shape, our optimisation here aims to find the 3D pose that leads to the 2D projection that best segments a given image. We extend our approach to track multiple objects from multiple views and propose novel enhancements at the pixel level based on temporal consistency. Finally, owing to the per pixel nature of much of the algorithm, we support our theoretical approach with a real-time GPU based implementation. We next use our rigid 3D tracker in two applications: (i) a driver assistance system, where the tracker is augmented with 2D traffic sign detections, which, unlike previous work, allows for the relevance of the traffic signs to the driver to be gauged and (ii) a robust, real time 3D hand tracker that uses data from an off-the-shelf accelerometer and articulated pose classification results from a multiclass SVM classifier. Finally, we explore deformable 2D/3D object tracking. Unlike previous works, we use a non-linear and probabilistic dimensionality reduction, called Gaussian Process Latent Variable Models, to learn spaces of shape. Segmentation becomes a minimisation of an image-driven energy function in the learned space. We can represent both 2D and 3D shapes which we compress with Fourier-based transforms, to keep inference tractable. We extend this method by learning joint shape-parameter spaces, which, novel to the literature, enable simultaneous segmentation and generic parameter recovery. These can describe anything from 3D articulated pose to eye gaze. We also propose two novel extensions to standard GP-LVM: a method to explore the multimodality in the joint space efficiently, by learning a mapping from the latent space to a space that encodes the similarity between shapes and a method for obtaining faster convergence and greater accuracy by use of a hierarchy of latent embeddings.

Thèse numérisée par la Division de la gestion de documents et des archives de l'Université de Montréal.

Tracking and understanding human gait is an important step towards improving elderly mobility and safety. This thesis presents a vision-based tracking system that estimates the 3D pose of a wheeled walker user's lower limbs with cameras mounted on the moving walker. The tracker estimates 3D poses from images of the lower limbs in the coronal plane in a dynamic, uncontrolled environment. It employs a probabilistic approach based on particle filtering with three different camera setups: a monocular RGB camera, binocular RGB cameras, and a depth camera. For the RGB cameras, observation likelihoods are designed to compare the colors and gradients of each frame with initial templates that are manually extracted. Two strategies are also investigated for handling appearance change of tracking target: increasing number of templates and using different representations of colors. For the depth camera, two observation likelihoods are developed: the first one works directly in the 3D space, while the second one works in the projected image space. Experiments are conducted to evaluate the performance of the tracking system with different users for all three camera setups. It is demonstrated that the trackers with the RGB cameras produce results with higher error as compared to the depth camera, and the strategies for handling appearance change improve tracking accuracy in general. On the other hand, the tracker with the depth sensor successfully tracks the 3D poses of users over the entire video sequence and is robust against unfavorable conditions such as partial occlusion, missing observations, and deformable tracking target.

Thèse numérisée par la Division de la gestion de documents et des archives de l'Université de Montréal

Tracking and understanding human gait is an important step towards improving elderly mobility and safety. This thesis presents a vision-based tracking system that estimates the 3D pose of a wheeled walker user's lower limbs with cameras mounted on the moving walker. The tracker estimates 3D poses from images of the lower limbs in the coronal plane in a dynamic, uncontrolled environment. It employs a probabilistic approach based on particle filtering with three different camera setups: a monocular RGB camera, binocular RGB cameras, and a depth camera. For the RGB cameras, observation likelihoods are designed to compare the colors and gradients of each frame with initial templates that are manually extracted. Two strategies are also investigated for handling appearance change of tracking target: increasing number of templates and using different representations of colors. For the depth camera, two observation likelihoods are developed: the first one works directly in the 3D space, while the second one works in the projected image space. Experiments are conducted to evaluate the performance of the tracking system with different users for all three camera setups. It is demonstrated that the trackers with the RGB cameras produce results with higher error as compared to the depth camera, and the strategies for handling appearance change improve tracking accuracy in general. On the other hand, the tracker with the depth sensor successfully tracks the 3D poses of users over the entire video sequence and is robust against unfavorable conditions such as partial occlusion, missing observations, and deformable tracking target.

Submitted by Natalia de Souza Gonçalves (natalia.goncalves@ufpe.br) on 2016-09-28T12:05:32Z No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) Givanio José de Melo - Rastreamento de objetos de grandes proporções - 2016.pdf: 16669202 bytes, checksum: cdbc131dfb4c4a78c9a942e89fbd9b80 (MD5) / Made available in DSpace on 2016-09-28T12:05:32Z (GMT). No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) Givanio José de Melo - Rastreamento de objetos de grandes proporções - 2016.pdf: 16669202 bytes, checksum: cdbc131dfb4c4a78c9a942e89fbd9b80 (MD5) Previous issue date: 2016-03-03 / FACEPE / Muitas aplicações necessitam ou se beneficiam de rastreamento de objetos 3D complexos, dentre elas: interfaces de interação, controle robótico e realidade aumentada são exemplos de sistemas que dependem desse tipo de implementação. A construção de um grande estádio de futebol por exemplo, incorpora várias etapas de inspeção visual, medições e controle, que hoje são realizadas de forma artesanal. Processos de produção de maquinários de grande porte como aqueles aplicados à produção de energia eólica, também comuns em estaleiros e indústrias do setor automobilístico e aeroespacial, são exemplos de onde os meios de produção atuais podem se beneficiar significativamente desse tipo de tecnologia. A área de pesquisa em visão computacional visa desenvolver soluções para problemas inerentes a esses processos de forma prática e pouco invasiva, embora o desenvolvimento de técnicas e práticas robustas para estruturas de grandes proporções ainda figure como um ramo inexplorado dessa área de pesquisa. O objetivo dessa dissertação é descrever a pesquisa realizada acerca da área de visão computacional com foco em rastreamento de objetos 3D e propor uma abordagem específica para aplicação em grandes estruturas. A mesma foi, portanto, concebida pela investigação, experimentação e análise de aplicação das técnicas de rastreamento 3D baseado em modelos, com ênfase na comparação dessas técnicas de acordo com o cenário de aplicação, focando no desenvolvimento do rastreamento dedicado a grandes estruturas. Conceitos de visão computacional, processamento digital de imagem e especificamente técnicas de rastreamento baseadas em imagens digitais e modelos tridimensionais são contextualizados nessa dissertação, além de propor uma nova abordagem de rastreamento baseado em modelo utilizando descritores de caracteristicas visuais, e rastreamento baseado em detecção. Os experimentos realizados e relatados nessa dissertação incluem múltiplos níveis de escala e complexidade de objetos, com o objetivo de avaliar tanto a eficiência das técnicas pré-existentes na literatura, como das técnicas implementadas utilizando a abordagem proposta. Os resultados obtidos evidenciam as diferenças existentes entre as abordagens de rastreamento baseado em modelo, de acordo com o tipo, quantidade e qualidade da informação utilizada, como também da técnica de rastreamento selecionada, por fluxo óptico ou por detecção. / Many applications can take benefit of 3D object tracking to be performed. Interaction interfaces, robotic control and augmented reality figure in this conjecture and depend on this kind of implementation. The building process of a big soccer stadium, for instance, includes various steps in visual inspection, measures and quality control, which one currently makes without assistance. Some means of production that can achieve gains using this technology are wind power generation, shipbuilding, car manufacturing, aircraft and aeroespacial building. Computer vision research aims to create new solutions for typical problems of these processes, in practical and non-invasive ways. Anyway, the tracking of big structures still figures as a non-explored branch of computer vision. The main objective for this master thesis is to report the research in computer vision, focusing in 3D object tracking, and propose a new specific approach for big object tracking applications. This master thesis was conceived by examination, experimentation and application analysis over 3D model based tracking applications, in order to compare the implementations in different scenarios and contribute to big structure tracking branch’s development. Inside this text is presented and discussed the basis of digital image processing and computer vision, specifically, this work focuses in 3D model based techniques to track real world objects in camera images. As the main contribution, a new approach is proposed for model based tracking using feature descriptors and tracking by detection. The tests presented, include multi-scale and multi-complexity objects in order to evaluate both, literature and proposed approaches, under the effectiveness aspect. The results obtained show clear differences between approaches in model based tracking, according to information (type, quantity, quality) and tracking basis (optical flow or tracking by detection).

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