Enhanced Augmented Reality Framework for Sports Entertainment Applications

Augmented Reality (AR) superimposes virtual information on real-world data, such as displaying useful information on videos/images of a scene. This dissertation presents an Enhanced AR (EAR) framework for displaying useful information on images of a sports game. The challenge in such applications is robust object detection and recognition. This is even more challenging when there is strong sunlight. We address the phenomenon where a captured image is degraded by strong sunlight. The developed framework consists of an image enhancement technique to improve the accuracy of subsequent player and face detection. The image enhancement is followed by player detection, face detection, recognition of players, and display of personal information of players. First, an algorithm based on Multi-Scale Retinex (MSR) is proposed for image enhancement. For the tasks of player and face detection, we use adaptive boosting algorithm with Haar-like features for both feature selection and classification. The player face recognition algorithm uses adaptive boosting with the LDA for feature selection and nearest neighbor classifier for classification. The framework can be deployed in any sports where a viewer captures images. Display of players-specific information enhances the end-user experience. Detailed experiments are performed on 2096 diverse images captured using a digital camera and smartphone. The images contain players in different poses, expressions, and illuminations. Player face recognition module requires players faces to be frontal or up to ?350 of pose variation. The work demonstrates the great potential of computer vision based approaches for future development of AR applications.COMSATS Institute of Information Technolog

Augmented Reality in Sport Broadcasting

For a large portion of its history, sport broadcasting has been stagnant when it comes to incorporating new and innovative technologies. However, due to declining viewership and consumer desire for customizable content, augmented reality graphics have begun to be incorporated into multiple sport broadcast products. In fact, the UEFA Champions League, NBA, NFL, and NHL have all used or indicated their intention to utilize AR graphics in future broadcasts. Considering that media rights revenue is the main source of revenue to sport properties and organizations, it is important to carefully consider how the core product (the broadcast) is presented. The study examined consumer attitudes and intentions towards AR in sport broadcasts by utilizing three types of broadcasts of an NBA game. One of the broadcasts was a traditional broadcast format with no AR enhancement and the other two were enhanced with AR graphics, a coach-mode broadcast that featured AR player tracking and play diagramming while the other enhanced broadcast, mascot-mode, featured AR graphics similar to a video game with over-the-top animations. Results of the current study provide insight into consumer preferences towards AR in sport broadcasting and guidance to sport properties planning to utilize broadcast AR graphics. Specifically, that sport consumers were significantly more likely to re-view (p \u3c .05) and recommend via word of mouth (p \u3c .05) the coach-mode AR than the mascot-mode AR. Sport involvement was a significant factor for how sport fans perceive the AR broadcast types through incorporating the perspective of the elaboration likelihood model

Detecting and tracking people in real-time

The problem of detecting and tracking people in images and video has been the subject of a great deal of research, but remains a challenging task. Being able to detect and track people would have an impact in a number of fields, such as driverless vehicles, automated surveillance, and human-computer interaction. The difficulties that must be overcome include coping with variations in appearance between different people, changes in lighting, and the ability to detect people across multiple scales. As well as having high accuracy, it is desirable for a technique to evaluate an image with low latency between receiving the image and producing a result. This thesis explores methods for detecting and tracking people in images and video. Techniques are implemented on a desktop computer, with an emphasis on low latency. The problem of detection is examined first. The well established integral channel features detector is introduced and reimplemented, and various novelties are implemented in regards to the features used by the detector. Results are given to quantify the accuracy and the speed of the developed detectors on the INRIA person dataset. The method is further extended by examining the prospect of using multiple classifiers in conjunction. It is shown that using a classifier with a version of the same classifier reflected in the vertical axis can improve performance. A novel method for clustering images of people to find modes of appearance is also presented. This involves using boosting classifiers to map a set of images to vectors, to which K-means clustering is applied. Boosting classifiers are then trained on these clustered datasets to create sets of multiple classifiers, and it is demonstrated that these sets of classifiers can be evaluated on images with only a small increase in the running time over single classifiers. The problem of single target tracking is addressed using the mean shift algorithm. Mean shift tracking works by finding the best colour match for a target from frame to frame. A novel form of mean shift tracking through scale is developed, and the problem of multiple target tracking is addressed by using boosting classifiers in conjunction with Kalman filters. Tests are carried out on the CAVIAR dataset, which gives representative examples of surveillance scenarios, to show the performance of the proposed approaches.Open Acces

Multicamera System for Automatic Positioning of Objects in Game Sports

Garantir um sistema com múltiplas câmaras que seja capaz de extrair dados 3D da posição de uma bola durante um evento desportivo, através da análise e teste de técnicas de visão computacional (calibração de câmaras e reconstrução 3D)

Sistema de visão computacional aplicado em análises de jogos de tênis

The field of artificial intelligence has played a very important role in current technological development, making machines have a perception of the world similar to humans and are able to perform many difficult tasks with good precision. In this sense, a significant part of artificial intelligence deals with systems that perform actions that require a computer vision system, which acts as a sensor providing high-level information about a given environment. A research area within the field of computational vision studies how to reconstruct and understand a 3D scene from the properties present in 2D images. The results of this line of research become very valuable due to the fact that some moves can be very difficult to be analyzed just by physically watching a game. Thus, computer vision tools are being increasingly used in sports applications, where technical decisions in a given move can be decisive for the match. A sport that has been highly explored in terms of research development is tennis due to its growing dissemination and financial relevance, being responsible for annual turnover of R$1.8 billion in Brazil alone. Thus, this master’s dissertation work aims to develop a system based on computer vision for analyzing tennis games. The implemented system captures videos during the tennis game through cameras installed on the court and processes the images obtained, applying machine learning methods and morphological operations, in order to locate the position of the ball, the lines of the court and the location of the players after processing. In addition, the algorithm determines the moment the ball bounces during the game and analyzes whether it occurred in or out of the field. From the results obtained, the system demonstrated robustness and reliability. These data are made available to players and judges both on the court during the game and through an Android application, also developed in this work. The application aims to allow all data resulting from processing to be accessed from mobile devices, providing the results quickly and accessible to the user.O campo de inteligência artificial tem desempenhado um papel muito importante no desenvolvimento tecnológico atual, fazendo com que as máquinas tenham uma percepção de mundo de forma semelhante à dos humanos e sejam capazes de realizar diversas tarefas difíceis com boa precisão. Neste sentido, uma parte significativa da inteligência artificial lida com sistemas que realizam ações que necessitam de um sistema de visão computacional, que age como um sensor fornecendo informações de alto nível sobre um determinado ambiente. Uma área de pesquisa dentro do campo de visão computacional estuda como reconstruir e compreender uma cena 3D a partir das propriedades presentes em imagens 2D. Os resultados dessa linha de pesquisa se tornam muito valiosos devido ao fato de que alguns lances podem ser muito difíceis de serem analisados somente assistindo fisicamente a uma partida. Assim, ferramentas de visão computacional estão sendo cada vez mais utilizadas em aplicações esportivas, onde as decisões técnicas em um determinado lance podem ser decisivas para a partida. Um esporte que vem sendo altamente explorado em termos de desenvolvimento de pesquisas é o tênis pela sua crescente disseminação e relevância financeira, sendo responsável por movimentar anualmente R$1,8 bilhão só no Brasil. Dessa forma, este trabalho de dissertação de mestrado objetiva o desenvolvimento de um sistema baseado em visão computacional para análise de jogos de tênis. O sistema implementado captura vídeos durante o jogo de tênis por meio de câmeras instaladas na quadra e processa as imagens obtidas, aplicando métodos de aprendizado de máquina e operações morfológicas, a fim de localizar a posição da bola, as linhas da quadra e a localização dos jogadores após o processamento. Além disso, o algoritmo determina o momento do quique da bola durante o jogo e analisa se este ocorreu dentro ou fora do campo. A partir dos resultados obtidos, o sistema demonstrou robustez e confiabilidade. Esses dados são disponibilizados aos jogadores e juízes tanto na quadra durante a realização do jogo quanto por meio de um aplicativo Android, também desenvolvido neste trabalho. O aplicativo tem por objetivo permitir que todos os dados resultantes do processamento sejam acessado a partir de dispositivos móveis, fornecendo os resultados de forma rápida e acessível ao usuário.FAPEMIG - Fundação de Amparo à Pesquisa do Estado de Minas Gerai

Exploring Sparse, Unstructured Video Collections of Places

The abundance of mobile devices and digital cameras with video capture makes it easy to obtain large collections of video clips that contain the same location, environment, or event. However, such an unstructured collection is difficult to comprehend and explore. We propose a system that analyses collections of unstructured but related video data to create a Videoscape: a data structure that enables interactive exploration of video collections by visually navigating — spatially and/or temporally — between different clips. We automatically identify transition opportunities, or portals. From these portals, we construct the Videoscape, a graph whose edges are video clips and whose nodes are portals between clips. Now structured, the videos can be interactively explored by walking the graph or by geographic map. Given this system, we gauge preference for different video transition styles in a user study, and generate heuristics that automatically choose an appropriate transition style. We evaluate our system using three further user studies, which allows us to conclude that Videoscapes provides significant benefits over related methods. Our system leads to previously unseen ways of interactive spatio-temporal exploration of casually captured videos, and we demonstrate this on several video collections