31 research outputs found

    FARSEC: A Reproducible Framework for Automatic Real-Time Vehicle Speed Estimation Using Traffic Cameras

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    Estimating the speed of vehicles using traffic cameras is a crucial task for traffic surveillance and management, enabling more optimal traffic flow, improved road safety, and lower environmental impact. Transportation-dependent systems, such as for navigation and logistics, have great potential to benefit from reliable speed estimation. While there is prior research in this area reporting competitive accuracy levels, their solutions lack reproducibility and robustness across different datasets. To address this, we provide a novel framework for automatic real-time vehicle speed calculation, which copes with more diverse data from publicly available traffic cameras to achieve greater robustness. Our model employs novel techniques to estimate the length of road segments via depth map prediction. Additionally, our framework is capable of handling realistic conditions such as camera movements and different video stream inputs automatically. We compare our model to three well-known models in the field using their benchmark datasets. While our model does not set a new state of the art regarding prediction performance, the results are competitive on realistic CCTV videos. At the same time, our end-to-end pipeline offers more consistent results, an easier implementation, and better compatibility. Its modular structure facilitates reproducibility and future improvements

    On-line Time Warping of Human Motion Sequences

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    Some application areas require motions to be time warped on-line as a motion is captured, aligning a partially captured motion to a complete prerecorded motion. For example movement training applications for dance and medical procedures, require on-line time warping for analysing and visually feeding back the accuracy of human motions as they are being performed. Additionally, real-time production techniques such as virtual production, in camera visual effects and the use of avatars in live stage performances, require on-line time warping to align virtual character performances to a live performer. The work in this thesis first addresses a research gap in the measurement of the alignment of two motions, proposing approaches based on rank correlation and evaluating them against existing distance based approaches to measuring motion similarity. The thesis then goes onto propose and evaluate novel methods for on-line time warping, which plot alignments in a forward direction and utilise forecasting and local continuity constraint techniques. Current studies into measuring the similarity of motions focus on distance based metrics for measuring the similarity of the motions to support motion recognition applications, leaving a research gap regarding the effectiveness of similarity metrics bases on correlation and the optimal metrics for measuring the alignment of two motions. This thesis addresses this research gap by comparing the performance of variety of similarity metrics based on distance and correlation, including novel combinations of joint parameterisation and correlation methods. The ability of each metric to measure both the similarity and alignment of two motions is independently assessed. This work provides a detailed evaluation of a variety of different approaches to using correlation within a similarity metric, testing their performance to determine which approach is optimal and comparing their performance against established distance based metrics. The results show that a correlation based metric, in which joints are parameterised using displacement vectors and correlation is measured using Kendall Tau rank correlation, is the optimal approach for measuring the alignment between two motions. The study also showed that similarity metrics based on correlation are better at measuring the alignment of two motions, which is important in motion blending and style transfer applications as well as evaluating the performance of time warping algorithms. It also showed that metrics based on distance are better at measuring the similarity of two motions, which is more relevant to motion recognition and classification applications. A number of approaches to on-line time warping have been proposed within existing research, that are based on plotting an alignment path backwards from a selected end-point within the complete motion. While these approaches work for discrete applications, such as recognising a motion, their lack of monotonic constraint between alignment of each frame, means these approaches do not support applications that require an alignment to be maintained continuously over a number of frames. For example applications involving continuous real-time visualisation, feedback or interaction. To solve this problem, a number of novel on-line time warping algorithms, based on forward plotting, motion forecasting and local continuity constraints are proposed and evaluated by applying them to human motions. Two benchmarks standards for evaluating the performance of on-line time warping algorithms are established, based on UTW time warping and compering the resulting alignment path with that produced by DTW. This work also proposes a novel approach to adapting existing local continuity constraints to a forward plotting approach. The studies within this thesis demonstrates that these time warping approaches are able to produce alignments of sufficient quality to support applications that require an alignment to be maintained continuously. The on-line time warping algorithms proposed in this study can align a previously recorded motion to a user in real-time, as they are performing the same action or an opposing action recorded at the same time as the motion being align. This solution has a variety of potential application areas including: visualisation applications, such as aligning a motion to a live performer to facilitate in camera visual effects or a live stage performance with a virtual avatar; motion feedback applications such as dance training or medical rehabilitation; and interaction applications such as working with Cobots

    Tuberculosis diagnosis from pulmonary chest x-ray using deep learning.

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    Doctoral Degree. University of KwaZulu-Natal, Durban.Tuberculosis (TB) remains a life-threatening disease, and it is one of the leading causes of mortality in developing countries. This is due to poverty and inadequate medical resources. While treatment for TB is possible, it requires an accurate diagnosis first. Several screening tools are available, and the most reliable is Chest X-Ray (CXR), but the radiological expertise for accurately interpreting the CXR images is often lacking. Over the years, CXR has been manually examined; this process results in delayed diagnosis, is time-consuming, expensive, and is prone to misdiagnosis, which could further spread the disease among individuals. Consequently, an algorithm could increase diagnosis efficiency, improve performance, reduce the cost of manual screening and ultimately result in early/timely diagnosis. Several algorithms have been implemented to diagnose TB automatically. However, these algorithms are characterized by low accuracy and sensitivity leading to misdiagnosis. In recent years, Convolutional Neural Networks (CNN), a class of Deep Learning, has demonstrated tremendous success in object detection and image classification task. Hence, this thesis proposed an efficient Computer-Aided Diagnosis (CAD) system with high accuracy and sensitivity for TB detection and classification. The proposed model is based firstly on novel end-to-end CNN architecture, then a pre-trained Deep CNN model that is fine-tuned and employed as a features extractor from CXR. Finally, Ensemble Learning was explored to develop an Ensemble model for TB classification. The Ensemble model achieved a new stateof- the-art diagnosis accuracy of 97.44% with a 99.18% sensitivity, 96.21% specificity and 0.96% AUC. These results are comparable with state-of-the-art techniques and outperform existing TB classification models.Author's Publications listed on page iii

    Advances in Image Processing, Analysis and Recognition Technology

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    For many decades, researchers have been trying to make computers’ analysis of images as effective as the system of human vision is. For this purpose, many algorithms and systems have previously been created. The whole process covers various stages, including image processing, representation and recognition. The results of this work can be applied to many computer-assisted areas of everyday life. They improve particular activities and provide handy tools, which are sometimes only for entertainment, but quite often, they significantly increase our safety. In fact, the practical implementation of image processing algorithms is particularly wide. Moreover, the rapid growth of computational complexity and computer efficiency has allowed for the development of more sophisticated and effective algorithms and tools. Although significant progress has been made so far, many issues still remain, resulting in the need for the development of novel approaches

    MultiFingerBubble: A 3D Bubble Cursor Variation for Dense Environments

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    In this work, we propose MultiFingerBubble, a new variation of the 3D Bubble Cursor. The 3D Bubble Cursor is sensitive to distractors in dense environments: the volume selection resizes to snap-to nearby targets. To prevent the cursor to constantly re-snap to neighboring targets, MultiFingerBubble includes multiple targets in the volume selection, and hence increases the targets effective width. Each target in the volume selection is associated with a specific finger. Users can then select a target by flexing its corresponding finger. We report on a controlled in-lab experiment to explore various design options regarding the number of fingers to use, and the target-to-finger mapping and its visualization. Our study results suggest that MultiFingerBubble is best used with three fingers and colored lines to reveal the mapping between targets and fingers

    Reconstruction et lissage de surfaces discrètes

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    Reconstruire une surface à partir des informations photométriques contenues dans une ou plusieurs images constitue un problème classique dans le domaine de la vision par ordinateur. Dans cette thèse, à travers un travail en collaboration avec des archéologues de l'IPGQ(Institut de Préhistoire et de Géologie du Quaternaire), nous nous sommes penchés sur le problème de reconstruction et d'extraction de paramètres de surfaces discrètes. Dans un premier temps, nous avons considéré le problème de reconstruction de surfaces à travers une approche discrète, en combinant les informations géométriques de la surface discrète que l'on reconstruit avec les informations photométriques contenues dans une ou plusieurs images. Nous avons pu définir une première méthode basée sur la propagation de contours discrets par niveaux d'iso-altitude. Même si cette approche a pu donner des résultats intéressants sur des images synthétiques, nous nous sommes orientés vers une autre approche beaucoup plus robuste. Cette deuxième méthode est basée sur la propagation de régions d'iso-altitude en considérant le contour d'iso-altitude de manière implicite. La reconstruction a pu montrer une grande résistance vis à vis du bruit et des informations photométriques. Cette méthode permet à travers les patchs de résoudre explicitement les ambiguïtés concaves/convexes lorsqu'une seule source lumineuse frontale est utilisée pour la reconstruction. De plus, étant donné que notre approche ne se base pas uniquement sur l'expression analytique de la fonction de réflectance (habituellement Lambertienne), nous avons pu effectuer la reconstruction d'objets réels spéculaires en considérant d'autres modèles de réflection tel que le modèle de Nayar. Enfin, nous avons pu montrer des résultats originaux permettant d'effectuer une reconstruction à partir de plusieurs dessins associés à plusieurs directions d'éclairage. Les résultats permettent d'envisager un concept original pour définir des formes à partir d'images associées à une surface imaginaire. Dans une deuxième partie, nous introduisons une nouvelle méthode réversible de lissage de surfaces discrètes. Cette méthode est basée sur l'estimation des caractéristiques du plan discret à partir d'un critère statistique et géométrique. Le critère statistique se base sur la répartition des différents types de surfels présents sur la surface, tandis que le critère géométrique est défini à partir des inégalités du plan discret. à partir de ces caractéristiques, nous définissons ensuite une surface à travers la projection des points discrets sur le plan tangent. Cette projection présente la propriété de transformer les points de Z3 dans R3 tout en étant réversible. La nouvelle surface Euclidienne résultante de cette transformation est à la fois utile pour l'extraction de paramètres géométriques et pour la visualisation sans aucune perte d'information par rapport à la surface discrète initiale.Shape reconstruction from shading informations contained in one or several images constitutes a classical problem in the field of computer vision. In this thesis, through a collaboration with archaeologists from the IPGQ (Institute of Prehistory and Quaternary Geology), we focus on the reconstruction and parameter extraction of discrete surfaces. First, we consider the problem of surface reconstruction using a discrete approach by combining geometric informations of the surface which is going to be reconstructed and photometric informations from one or several shading images. We have defined a new approach based on the propagation of discrete equal height contours. This approach gives good results on simple synthetic images, but we have chosen another approach in order to obtain more robustness on real images. This second method is based on the same idea through the propagation of equal height regions (called patch). The resulting reconstruction method gives robust results both on the point of view of photometric informations and noise. Moreover, it allows to explicitly solve the concave/convex ambiguity when only one light source direction (in the direction of the observer) is used for the reconstruction. Furthermore, since the reconstruction does not use the analytical expression of the reflectance map (usually Lambertian), the reconstruction was applied with other reflectance models such as the specular Nayar's model of reflectance. Finally, we have presented some original reconstructions obtained from several drawings associated with several light source directions. These results can offer new perspectives to define an intuitive way for shape modelling from shading images. In a second part, we introduce a new reversible method for discrete surface smoothing. This method is based on the estimation of the discrete plan characteristics using a statistical and geometrical criteria. The statistical criteria use the allocation of different types of discrete surface elements called surfel and the geometrical criteria is defined from the inequalities of the discrete plane. From the characteristics of the discrete tangent plane, a surface net is deduced by projecting the centers of voxels to the real tangent plane. This projection transforming the surface points from Z3 to R3 has the property to be reversible. Thus, it allows to obtain a new Euclidean surface net which can be used both for the extraction of geometrical parameters and for visualization

    Nature-inspired algorithms for solving some hard numerical problems

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    Optimisation is a branch of mathematics that was developed to find the optimal solutions, among all the possible ones, for a given problem. Applications of optimisation techniques are currently employed in engineering, computing, and industrial problems. Therefore, optimisation is a very active research area, leading to the publication of a large number of methods to solve specific problems to its optimality. This dissertation focuses on the adaptation of two nature inspired algorithms that, based on optimisation techniques, are able to compute approximations for zeros of polynomials and roots of non-linear equations and systems of non-linear equations. Although many iterative methods for finding all the roots of a given function already exist, they usually require: (a) repeated deflations, that can lead to very inaccurate results due to the problem of accumulating rounding errors, (b) good initial approximations to the roots for the algorithm converge, or (c) the computation of first or second order derivatives, which besides being computationally intensive, it is not always possible. The drawbacks previously mentioned served as motivation for the use of Particle Swarm Optimisation (PSO) and Artificial Neural Networks (ANNs) for root-finding, since they are known, respectively, for their ability to explore high-dimensional spaces (not requiring good initial approximations) and for their capability to model complex problems. Besides that, both methods do not need repeated deflations, nor derivative information. The algorithms were described throughout this document and tested using a test suite of hard numerical problems in science and engineering. Results, in turn, were compared with several results available on the literature and with the well-known Durand–Kerner method, depicting that both algorithms are effective to solve the numerical problems considered.A Optimização é um ramo da matemática desenvolvido para encontrar as soluções óptimas, de entre todas as possíveis, para um determinado problema. Actualmente, são várias as técnicas de optimização aplicadas a problemas de engenharia, de informática e da indústria. Dada a grande panóplia de aplicações, existem inúmeros trabalhos publicados que propõem métodos para resolver, de forma óptima, problemas específicos. Esta dissertação foca-se na adaptação de dois algoritmos inspirados na natureza que, tendo como base técnicas de optimização, são capazes de calcular aproximações para zeros de polinómios e raízes de equações não lineares e sistemas de equações não lineares. Embora já existam muitos métodos iterativos para encontrar todas as raízes ou zeros de uma função, eles usualmente exigem: (a) deflações repetidas, que podem levar a resultados muito inexactos, devido ao problema da acumulação de erros de arredondamento a cada iteração; (b) boas aproximações iniciais para as raízes para o algoritmo convergir, ou (c) o cálculo de derivadas de primeira ou de segunda ordem que, além de ser computacionalmente intensivo, para muitas funções é impossível de se calcular. Estas desvantagens motivaram o uso da Optimização por Enxame de Partículas (PSO) e de Redes Neurais Artificiais (RNAs) para o cálculo de raízes. Estas técnicas são conhecidas, respectivamente, pela sua capacidade de explorar espaços de dimensão superior (não exigindo boas aproximações iniciais) e pela sua capacidade de modelar problemas complexos. Além disto, tais técnicas não necessitam de deflações repetidas, nem do cálculo de derivadas. Ao longo deste documento, os algoritmos são descritos e testados, usando um conjunto de problemas numéricos com aplicações nas ciências e na engenharia. Os resultados foram comparados com outros disponíveis na literatura e com o método de Durand–Kerner, e sugerem que ambos os algoritmos são capazes de resolver os problemas numéricos considerados

    Joint appearance and motion model for multi-class multi-object tracking

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    Model-free tracking is a widely-accepted approach to track an arbitrary object in a video using a single frame annotation with no further prior knowledge about the object of interest. Extending this problem to track multiple objects is really challenging because: a) the tracker is not aware of the objects’ type while trying to distinguish them from background (detection task) , and b) The tracker needs to distinguish one object from other potentially similar objects (data association task) to generate stable trajectories. In order to track multiple arbitrary objects, most existing model-free tracking approaches rely on tracking each target individually by updating their appearance model independently. Therefore, in this scenario they often fail to perform well due to confusion between the appearance of similar objects, their sudden appearance changes and occlusion. To tackle this problem, we propose to use both appearance and motion models, and to learn them jointly using graphical models and deep neural networks features. We introduce an indicator variable to predict sudden appearance change and/or occlusion. When these happen, our model does not update the appearance model thus avoiding using the background and/or incorrect object to update the appearance of the object of interest mistakenly, and relies on our motion model to track. Moreover, we consider the correlation among all targets, and seek the joint optimal locations for all targets simultaneously as a graphical model inference problem. We learn the joint parameters for both appearance model and motion model in an online fashion under the framework of LaRank. Experiment results show that our method outperforms the state-of-the-art.Thesis (Ph.D.) -- University of Adelaide, School of Computer Science, 201

    Gaze-Based Human-Robot Interaction by the Brunswick Model

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    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

    Change detection in combination with spatial models and its effectiveness on underwater scenarios

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    This thesis proposes a novel change detection approach for underwater scenarios and combines it with different especially developed spatial models, this allows accurate and spatially coherent detection of any moving objects with a static camera in arbitrary environments. To deal with the special problems of underwater imaging pre-segmentations based on the optical flow and other special adaptions were added to the change detection algorithm so that it can better handle typical underwater scenarios like a scene crowded by a whole fish swarm
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