8 research outputs found

    Deep-Learning-Based Computer Vision Approach For The Segmentation Of Ball Deliveries And Tracking In Cricket

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    There has been a significant increase in the adoption of technology in cricket recently. This trend has created the problem of duplicate work being done in similar computer vision-based research works. Our research tries to solve one of these problems by segmenting ball deliveries in a cricket broadcast using deep learning models, MobileNet and YOLO, thus enabling researchers to use our work as a dataset for their research. The output from our research can be used by cricket coaches and players to analyze ball deliveries which are played during the match. This paper presents an approach to segment and extract video shots in which only the ball is being delivered. The video shots are a series of continuous frames that make up the whole scene of the video. Object detection models are applied to reach a high level of accuracy in terms of correctly extracting video shots. The proof of concept for building large datasets of video shots for ball deliveries is proposed which paves the way for further processing on those shots for the extraction of semantics. Ball tracking in these video shots is also done using a separate RetinaNet model as a sample of the usefulness of the proposed dataset. The position on the cricket pitch where the ball lands is also extracted by tracking the ball along the y-axis. The video shot is then classified as a full-pitched, good-length or short-pitched delivery

    Table tennis event detection and classification

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    It is well understood that multiple video cameras and computer vision (CV) technology can be used in sport for match officiating, statistics and player performance analysis. A review of the literature reveals a number of existing solutions, both commercial and theoretical, within this domain. However, these solutions are expensive and often complex in their installation. The hypothesis for this research states that by considering only changes in ball motion, automatic event classification is achievable with low-cost monocular video recording devices, without the need for 3-dimensional (3D) positional ball data and representation. The focus of this research is a rigorous empirical study of low cost single consumer-grade video camera solutions applied to table tennis, confirming that monocular CV based detected ball location data contains sufficient information to enable key match-play events to be recognised and measured. In total a library of 276 event-based video sequences, using a range of recording hardware, were produced for this research. The research has four key considerations: i) an investigation into an effective recording environment with minimum configuration and calibration, ii) the selection and optimisation of a CV algorithm to detect the ball from the resulting single source video data, iii) validation of the accuracy of the 2-dimensional (2D) CV data for motion change detection, and iv) the data requirements and processing techniques necessary to automatically detect changes in ball motion and match those to match-play events. Throughout the thesis, table tennis has been chosen as the example sport for observational and experimental analysis since it offers a number of specific CV challenges due to the relatively high ball speed (in excess of 100kph) and small ball size (40mm in diameter). Furthermore, the inherent rules of table tennis show potential for a monocular based event classification vision system. As the initial stage, a proposed optimum location and configuration of the single camera is defined. Next, the selection of a CV algorithm is critical in obtaining usable ball motion data. It is shown in this research that segmentation processes vary in their ball detection capabilities and location out-puts, which ultimately affects the ability of automated event detection and decision making solutions. Therefore, a comparison of CV algorithms is necessary to establish confidence in the accuracy of the derived location of the ball. As part of the research, a CV software environment has been developed to allow robust, repeatable and direct comparisons between different CV algorithms. An event based method of evaluating the success of a CV algorithm is proposed. Comparison of CV algorithms is made against the novel Efficacy Metric Set (EMS), producing a measurable Relative Efficacy Index (REI). Within the context of this low cost, single camera ball trajectory and event investigation, experimental results provided show that the Horn-Schunck Optical Flow algorithm, with a REI of 163.5 is the most successful method when compared to a discrete selection of CV detection and extraction techniques gathered from the literature review. Furthermore, evidence based data from the REI also suggests switching to the Canny edge detector (a REI of 186.4) for segmentation of the ball when in close proximity to the net. In addition to and in support of the data generated from the CV software environment, a novel method is presented for producing simultaneous data from 3D marker based recordings, reduced to 2D and compared directly to the CV output to establish comparative time-resolved data for the ball location. It is proposed here that a continuous scale factor, based on the known dimensions of the ball, is incorporated at every frame. Using this method, comparison results show a mean accuracy of 3.01mm when applied to a selection of nineteen video sequences and events. This tolerance is within 10% of the diameter of the ball and accountable by the limits of image resolution. Further experimental results demonstrate the ability to identify a number of match-play events from a monocular image sequence using a combination of the suggested optimum algorithm and ball motion analysis methods. The results show a promising application of 2D based CV processing to match-play event classification with an overall success rate of 95.9%. The majority of failures occur when the ball, during returns and services, is partially occluded by either the player or racket, due to the inherent problem of using a monocular recording device. Finally, the thesis proposes further research and extensions for developing and implementing monocular based CV processing of motion based event analysis and classification in a wider range of applications

    Media of things : supporting the production and consumption of object-based media with the internet of things

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    Ph. D. Thesis.Visual media consumption habits are in a constant state of flux, predicting which platforms and consumption mediums will succeed and which will fail is a fateful business. Virtual Reality and Augmented Reality could be the 3D TVs that went before them, or they could push forward a new level of content immersion and radically change media production forever. Content producers are constantly trying to adapt to these shifts in habits and respond to new technologies. Smaller independent studios buoyed by their new-found audience penetration through sites like YouTube and Facebook can inherently respond to these emerging technologies faster, not weighed down by the “legacy” many. Broadcasters such as the BBC are keen to evolve their content to respond to the challenges of this new world. Producing content that is both more compelling in terms of immersion, and more responsive to technological advances in terms of input and output mediums. This is where the concept of Object-based Broadcasting was born, content that is responsive to the user consuming their content on a phone over a short period of time whilst also providing an immersive multi-screen experience for a smart home environment. One of the primary barriers to the development of Object-based Media is in a feasible set of mechanisms to generate supporting assets and adequately exploit the input and output mediums of the modern home. The underlying question here is how we build these experiences, we obviously can’t produce content for each of the thousands of combinations of devices and hardware we have available to us. I view this challenge to content makers as one of a distinct lack of descriptive and abstract detail at both ends of the production pipeline. In investigating the contribution that the Internet of Things may have to this space I first look to create well described assets in productions using embedded sensing. Detecting non-visual actions and generating detail not possible from vision alone. I then look to exploit existing datasets from production and consumption environments to gain greater understanding of generated media assets and a means to coordinate input/output in the home. Finally, I investigate the opportunities for rich and expressive interaction with devices and content in the home exploiting favourable characteristics of existing interfaces to construct a compelling control interface to Smart Home devices and Object-based experiences. I resolve that the Internet of Things is vital to the development of Object-based Broadcasting and its wider roll-out.British Broadcasting Corporatio

    Factors impacting the introduction of information technology usage in netball coaching

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    With the growth in the use of technology in sports, there has been an improvement in sporting performances. Some may argue that the two go hand in hand, while others will write it off as coincidence. Nevertheless the use of technology in sport is seen on a daily basis. Cricket uses Hotspot, tennis uses Hawk-Eye and rugby uses slow motion video replays. In these sports codes, technology is already an aid to umpires. Little is known, however, about the technologies used to assist coaches in sports codes such as netball. This study investigated the factors impacting the introduction of information technology in the coaching of netball. The study commenced with using the term technology in the broader sense of the word to gain an understanding from netball coaches as to how technology could be incorporated into the sport. It was later narrowed down more specifically to computer technologies. The investigation was done at the Spar National Netball Championships in 2012, where coaches were surveyed about the preparation for the tournament of the provincial teams. The surveys included questions to coaches regarding the technologies used in preparation for a national tournament. The results obtained from the coaches were used to identify the current technologies used. Interviews were conducted after the analysis of the initial results to probe into the potential use of social media as a tool to assist coaches. Based on the results of the study, a number of factors that impact on the introduction of technology in the coaching of netball were identified. The factors and basic guidelines were validated through expert focus groups. Based on the findings from the experts, the factors and guidelines were refined. It is envisaged that the findings from this research can be used to assist netball coaches in deciding how to introduce the use of information technology into the sport

    The Trinity Reporter, Winter 2016

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    https://digitalrepository.trincoll.edu/reporter/2150/thumbnail.jp

    Kenyon Alumni Magazine - Spring 2023

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    https://digital.kenyon.edu/kcab/1304/thumbnail.jp

    Draw My Life: An analysis of the quantity and typology of emotional linguistic content in self-identified female and male YouTubers’ life narratives

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    La presente investigaciĂłn tiene como objetivo determinar similitudes y diferencias en la cantidad y tipologĂ­a de expresiones relacionadas con la emociĂłn – referencias tanto implĂ­citas como explĂ­citas a “feelings, moods and all kinds of affective experience” [sentimientos, estados de ĂĄnimo y todo tipo de experiencias afectivas] (Mackenzie y Alba-Juez, 2019, p. 15) – de 100 personas autoidentificadas como mujeres (con un corpus de 248.613 palabras en total) y 100 personas autoidentificadas como hombres (con un corpus de 227.979 palabras en total) en sus vĂ­deos autobiogrĂĄficos dentro del gĂ©nero Draw My Life de YouTube. El proyecto se sustenta en la nociĂłn de Lutz (1990, p. 151) de que “any discourse on emotion is also, at least implicitly, a discourse on gender” [cualquier discurso sobre la emociĂłn es tambiĂ©n, al menos implĂ­citamente, un discurso sobre gĂ©nero], con frecuentes suposiciones en investigaciones previas sobre las expectativas sociales relacionadas con la “greater emotional expressivity” [mayor expresividad emocional] de las mujeres (Chaplin, 2015, p. 14) y la “restrictive emotionality” [emocionalidad restrictiva] de los hombres (O’Neil, Good, & Holmes, 1995, p. 176). Con el objetivo de obtener datos completos y fiables sobre las expresiones relacionadas con las emociones de los YouTubers femeninos y masculinos, el estudio combina mĂ©todos de investigaciĂłn cuantitativos y cualitativos que se basan en varias herramientas computerizadas, asĂ­ como en procesos de anotaciĂłn manual. En particular, se adopta un marco de anĂĄlisis crĂ­tico del discurso basado en corpus, motivado por la suposiciĂłn de Baker et al. (2008, p. 227) de que las investigaciones de LingĂŒĂ­stica de Corpus “offer the researcher a reasonably high degree of objectivity; that is, they enable the researcher to approach the texts (or text surface) (relatively) free from any preconceived or existing notions regarding their linguistic or semantic/pragmatic content” [ofrecen al investigador un grado razonablemente alto de objetividad; es decir, permiten al investigador acercarse a los textos (o la superficie del texto) (relativamente) libre de cualquier nociĂłn preconcebida o existente sobre su contenido lingĂŒĂ­stico o semĂĄntico/ pragmĂĄtico]. El trabajo se enmarca dentro del dominio de los Estudios de Discurso Asistidos por Corpus, definido por Partington, Duguid y Taylor (2013, p. 10) como “that set of studies into the form and/or function of language which incorporate the use of computerised corpora in their analysis” [ese conjunto de estudios sobre la forma y/o la funciĂłn del lenguaje que incorporan el uso de corpus informatizados en su anĂĄlisis”]. Las herramientas informĂĄticas especĂ­ficas que se utilizan en el anĂĄlisis de los datos de Draw My Life relacionados con sentimientos/emociones son Lingmotif, LIWC2015 (Linguistic Inquiry and Word Count) y Wmatrix4
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