25 research outputs found

    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

    Biometric Systems

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    Because of the accelerating progress in biometrics research and the latest nation-state threats to security, this book's publication is not only timely but also much needed. This volume contains seventeen peer-reviewed chapters reporting the state of the art in biometrics research: security issues, signature verification, fingerprint identification, wrist vascular biometrics, ear detection, face detection and identification (including a new survey of face recognition), person re-identification, electrocardiogram (ECT) recognition, and several multi-modal systems. This book will be a valuable resource for graduate students, engineers, and researchers interested in understanding and investigating this important field of study

    Modeling Humans at Rest with Applications to Robot Assistance

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    Humans spend a large part of their lives resting. Machine perception of this class of body poses would be beneficial to numerous applications, but it is complicated by line-of-sight occlusion from bedding. Pressure sensing mats are a promising alternative, but data is challenging to collect at scale. To overcome this, we use modern physics engines to simulate bodies resting on a soft bed with a pressure sensing mat. This method can efficiently generate data at scale for training deep neural networks. We present a deep model trained on this data that infers 3D human pose and body shape from a pressure image, and show that it transfers well to real world data. We also present a model that infers pose, shape and contact pressure from a depth image facing the person in bed, and it does so in the presence of blankets. This model similarly benefits from synthetic data, which is created by simulating blankets on the bodies in bed. We evaluate this model on real world data and compare it to an existing method that requires RGB, depth, thermal and pressure imagery in the input. Our model only requires an input depth image, yet it is 12% more accurate. Our methods are relevant to applications in healthcare, including patient acuity monitoring and pressure injury prevention. We demonstrate this work in the context of robotic caregiving assistance, by using it to control a robot to move to locations on a person’s body in bed.Ph.D

    Understanding Person Identification Through Gait

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    Gait recognition is the process of identifying humans from their bipedal locomotion such as walking or running. As such, gait data is privacy sensitive information and should be anonymized where possible. With the rise of higher quality gait recording techniques, such as depth cameras or motion capture suits, an increasing amount of detailed gait data is captured and processed. Introduction and rise of the Metaverse is but one popular application scenario in which the gait of users is transferred onto digital avatars. As a first step towards developing effective anonymization techniques for high-quality gait data, we study different aspects of movement data to quantify their contribution to gait recognition. We first extract categories of features from the literature on human gait perception and then design experiments for each category to assess how much the information they contain contributes to recognition success. Our results show that gait anonymization will be challenging, as the data is highly redundant and interdependent

    Development of a non-invasive motion capture system for swimming biomechanics

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    Sports researchers and coaches currently have no practical tool that can accurately and rapidly measure the 3D kinematics of swimmers. Established motion capture methods in biomechanics are not well suited for underwater use, either because they i) are not accurate enough (like depth-based systems, or the visual hull), ii) would impair the movement of swimmers (like sensor- and marker-based systems), or iii) are too time consuming (like manual digitisation). The ideal for swimming motion capture would be a markerless motion capture system that only requires a few cameras. Such a system would automatically extract silhouettes and 2D joint locations from the videos recorded by the cameras, and fit a generic 3D body model to these constraints. The main challenge in developing such a system for swimming motion capture lies in the development of algorithms for silhouette extraction and 2D pose detection (i.e., localisation of joints in image coordinates), which need to perform well on images of swimmers—a task that currently available algorithms fail. The aim of this PhD was the development of such algorithms. Existing datasets do not contain images of swimmers, making it impossible to train algorithms that would perform well in this domain. Therefore, during the PhD two datasets of images of swimmers were constructed and hand-labelled: one, called Scylla, for silhouette extraction (3,100 images); and one, called Charybdis, for 2D pose detection (8,000 images). Scylla and Charybdis are the first datasets developed specifically for training algorithms to perform well on images of swimmers. Indeed, using these datasets, two algorithms were developed during this PhD: FISHnet, for silhouette extraction; and POSEidon, for 2D pose detection. The novelty of FISHnet (which outperformed state-of-the-art algorithms on Scylla) lies in its ability to predict outputs at the same resolution as the inputs, allowing it to reconstruct fine-grained silhouettes. The novelty of POSEidon lies in its unique structure, which allows it to directly regress the x and y coordinates of joints without needing heatmaps. POSEidon is almost as accurate as humans at locating the spinal joints of swimmers, which are essential constraints onto which to fit 3D models. Using these two algorithms, researchers will, in the future, be able to assemble a markerless motion capture system for swimming, which will contribute to improving our understanding of swimming biomechanics, as well as providing coaches a tool with which to monitor the technique of swimmers

    Перспективы развития фундаментальных наук. Т. 7 : IT-технологии и электроника

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    Сборник содержит труды участников XVIII Международной конференции студентов, аспирантов и молодых учёных «Перспективы развития фундаментальных наук», представленные на секции «IT-технологии и электроника». Для студентов, аспирантов, молодых ученых и преподавателей, специализирующихся в области интеллектуальных систем управления, автоматизированных систем обработки информации и управления, информационной безопасности, наноэлектроники, получения и исследования наноматериалов, оптоэлектроники и нанофотоники, плазменной эмиссионной электроники, интеллектуальной силовой электроники, СВЧэлектроники, систем радиолокации, телевидения, радиосвязи, радиометрии и распространения волн радиочастотного и акустического диапазонов, а также импульсных и радиочастотных измерениях

    Sistema de orientação automática para um vibrómetro laser

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    Mestrado em Engenharia MecânicaThe study of material properties, is in many times done by observation of the material behavior under some vibrations. This vibrations can be measure with a laser vibrometer which has a laser beam that measures using the doppler e ect. This device is available at University of Aveiro, but it is a single point vibrometer, which means that the beam direction is the same as the vibrometer, meaning that, to measure another point it is necessary to move the device. To avoid such issue, it was incorporates, on a system on which a device that de ects the laser beam into desirable locations was added. The process of choosing the points is made using a Kinect which acts as a 3D scanner. To de ect the laser beam of the vibrometer, a galvanometer was used which control is made by an Arduino. The Arduino communicates with MATLAB via Serial communication. Several numerical strategies to de ne automatically a measuring mesh are tested in order to identify their limitations and advantages.O estudo das propriedades dos materiais é muitas vezes realizado a partir da observação do seu comportamento sobre determinados estímulos, tal como vibrações. Estas vibrações podem ser medidas com recurso a um vibrómetro laser sendo que este dispositivo emite um feixe laser e efetua as medições com recurso ao efeito de doppler. Tal dispositivo está disponível na Universidade de Aveiro, sendo este um vibrómetro de ponto único, o que significa que a direção do feixe laser é a direção do vibrómetro, sendo que para obter uma medição noutro ponto é necessário mover o dispositivo. De modo a ultrapassar essa limitação, foi criado um dispositivo onde é incorporado o vibrómetro laser de modo a direcionar o feixe laser para posições pré-definidas. A escolha dos pontos para medição é feita com recurso, a uma câmera Kinect que atua como um scanner 3D. Para deflectir o feixe laser proveniente do vibrómetro foi usado um galvanómetro de espelhos cujo controlo é feito a partir de um Arduino Uno que comunica com o software em MATLAB através de comunicação serie. São analisadas diversas metodologias de definição automática de malha de pontos
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