TRAJECTORY STUDY OF BALLROOM DANCE USING MILLISECOND VIDEO ANALYSIS

A short video (3 s) of the natural turn movements of ballroom dance was analyzed using two-dimensional trajectory analysis to demonstrate precise verification of the movement. The movements were recorded with a high-speed camera (240 Hz), and the trajectory was plotted at 4 ms intervals. The precise trajectories of test subjects’ movements were successfully monitored by making them wear LED lights on their necks, elbows, waists, and knees. The differences between the trajectories of an experienced subject’s movement and that of a beginner were clearly indicated, even when those movements occurred over short durations. The differences were also evident from a velocity analysis of the same video data. Our low-cost method can be applied to ballroom dance education, even in a personal dance studio

An effective trajectory-based algorithm for ball detection and tracking with application to the analysis of broadcast sports video

Ph.DDOCTOR OF PHILOSOPH

Game Plan: What AI can do for Football, and What Football can do for AI

The rapid progress in artificial intelligence (AI) and machine learning has opened unprecedented analytics possibilities in various team and individual sports, including baseball, basketball, and tennis. More recently, AI techniques have been applied to football, due to a huge increase in data collection by professional teams, increased computational power, and advances in machine learning, with the goal of better addressing new scientific challenges involved in the analysis of both individual players’ and coordinated teams’ behaviors. The research challenges associated with predictive and prescriptive football analytics require new developments and progress at the intersection of statistical learning, game theory, and computer vision. In this paper, we provide an overarching perspective highlighting how the combination of these fields, in particular, forms a unique microcosm for AI research, while offering mutual benefits for professional teams, spectators, and broadcasters in the years to come. We illustrate that this duality makes football analytics a game changer of tremendous value, in terms of not only changing the game of football itself, but also in terms of what this domain can mean for the field of AI. We review the state-of-theart and exemplify the types of analysis enabled by combining the aforementioned fields, including illustrative examples of counterfactual analysis using predictive models, and the combination of game-theoretic analysis of penalty kicks with statistical learning of player attributes. We conclude by highlighting envisioned downstream impacts, including possibilities for extensions to other sports (real and virtual)

Real-time Selection of Video Streams for Live TV Broadcasting Based on Query-by-Example Using a 3D Model

The emergence of low-cost cameras with nearly professional features in the consumer market represents a new important source of video information. For example, using an increasing number of these cameras in live TV broadcastings enables obtaining varied contents without affecting the production costs. However, searching for interesting shots (e.g., a certain view of a specific car in a race) among many video sources in real-time can be difficult for a Technical Director (TD). So, TDs require a mechanism to easily and precisely represent the kind of shot they want to obtain abstracting them from the need to be aware of all the views provided by the cameras. In this paper we present our proposal to help a TD to visually define, using an interface for the definition of 3D scenes, an interesting sample view of one or more objects in the scenario. We recreate the views of the cameras in a 3D engine and apply 3D geometric computations on their virtual view, instead of analyzing the real images they provide, to enable an efficient and precise real-time selection. Specifically, our system computes a similarity measure to rank the candidate cameras. Moreover, we present a prototype of the system and an experimental evaluation that shows the interest of our proposal

Semantic Management of Location-Based Services in Wireless Environments

En los últimos años el interés por la computación móvil ha crecido debido al incesante uso de dispositivos móviles (por ejemplo, smartphones y tablets) y su ubicuidad. El bajo coste de dichos dispositivos unido al gran número de sensores y mecanismos de comunicación que equipan, hace posible el desarrollo de sistemas de información útiles para sus usuarios. Utilizando un cierto tipo especial de sensores, los mecanismos de posicionamiento, es posible desarrollar Servicios Basados en la Localización (Location-Based Services o LBS en inglés) que ofrecen un valor añadido al considerar la localización de los usuarios de dispositivos móviles para ofrecerles información personalizada. Por ejemplo, se han presentado numerosos LBS entre los que se encuentran servicios para encontrar taxis, detectar amigos en las cercanías, ayudar a la extinción de incendios, obtener fotos e información de los alrededores, etc. Sin embargo, los LBS actuales están diseñados para escenarios y objetivos específicos y, por lo tanto, están basados en esquemas predefinidos para el modelado de los elementos involucrados en estos escenarios. Además, el conocimiento del contexto que manejan es implícito; razón por la cual solamente funcionan para un objetivo específico. Por ejemplo, en la actualidad un usuario que llega a una ciudad tiene que conocer (y comprender) qué LBS podrían darle información acerca de medios de transporte específicos en dicha ciudad y estos servicios no son generalmente reutilizables en otras ciudades. Se han propuesto en la literatura algunas soluciones ad hoc para ofrecer LBS a usuarios pero no existe una solución general y flexible que pueda ser aplicada a muchos escenarios diferentes. Desarrollar tal sistema general simplemente uniendo LBS existentes no es sencillo ya que es un desafío diseñar un framework común que permita manejar conocimiento obtenido de datos enviados por objetos heterogéneos (incluyendo datos textuales, multimedia, sensoriales, etc.) y considerar situaciones en las que el sistema tiene que adaptarse a contextos donde el conocimiento cambia dinámicamente y en los que los dispositivos pueden usar diferentes tecnologías de comunicación (red fija, inalámbrica, etc.). Nuestra propuesta en la presente tesis es el sistema SHERLOCK (System for Heterogeneous mobilE Requests by Leveraging Ontological and Contextual Knowledge) que presenta una arquitectura general y flexible para ofrecer a los usuarios LBS que puedan serles interesantes. SHERLOCK se basa en tecnologías semánticas y de agentes: 1) utiliza ontologías para modelar la información de usuarios, dispositivos, servicios, y el entorno, y un razonador para manejar estas ontologías e inferir conocimiento que no ha sido explicitado; 2) utiliza una arquitectura basada en agentes (tanto estáticos como móviles) que permite a los distintos dispositivos SHERLOCK intercambiar conocimiento y así mantener sus ontologías locales actualizadas, y procesar peticiones de información de sus usuarios encontrando lo que necesitan, allá donde esté. El uso de estas dos tecnologías permite a SHERLOCK ser flexible en términos de los servicios que ofrece al usuario (que son aprendidos mediante la interacción entre los dispositivos), y de los mecanismos para encontrar la información que el usuario quiere (que se adaptan a la infraestructura de comunicación subyacente)

Multi-sensor human action recognition with particular application to tennis event-based indexing

The ability to automatically classify human actions and activities using vi- sual sensors or by analysing body worn sensor data has been an active re- search area for many years. Only recently with advancements in both fields and the ubiquitous nature of low cost sensors in our everyday lives has auto- matic human action recognition become a reality. While traditional sports coaching systems rely on manual indexing of events from a single modality, such as visual or inertial sensors, this thesis investigates the possibility of cap- turing and automatically indexing events from multimodal sensor streams. In this work, we detail a novel approach to infer human actions by fusing multimodal sensors to improve recognition accuracy. State of the art visual action recognition approaches are also investigated. Firstly we apply these action recognition detectors to basic human actions in a non-sporting con- text. We then perform action recognition to infer tennis events in a tennis court instrumented with cameras and inertial sensing infrastructure. The system proposed in this thesis can use either visual or inertial sensors to au- tomatically recognise the main tennis events during play. A complete event retrieval system is also presented to allow coaches to build advanced queries, which existing sports coaching solutions cannot facilitate, without an inordi- nate amount of manual indexing. The event retrieval interface is evaluated against a leading commercial sports coaching tool in terms of both usability and efficiency

Leaders in stewardship

MU Operations, like most divisions across campus, saw substantial budget cuts again in Fiscal Year 2016. In the past two years, our division lost 88 employees requiring everyone else to take on additional duties to accomplish an ever-growing list of projects. While we stress being environmental and financial stewards of Mizzou’s resources, we have taken that concept to the next level, finding ways to accomplish more with less. Faculty and staff helped soften the blow by working with Campus Facilities – Facility Operations to empty their office trash and recyclables, freeing up custodians’ time to handle Mizzou’s recycling, formerly a volunteer effort. We strive to provide a favorable return on investment across campus, whether it be maintaining the beautiful landscape that helps attract prospective students to providing a safe campus, which many of our departments contribute to including MU Police, Environmental Health & Safety and Campus Facilities. We also offer leisure activities for the community-at-large with world-class artists performing at Missouri Theatre and Jesse Auditorium; culinary delights at the University Club and Catering; and 18 holes on the beautifully maintained A.L. Gustin Golf Course, the nation’s first Audubon Certified university course. These self-funded departments are looking for new ways to reach their audiences and will continue to do so in the coming months. While we have always had a large student staff, we work hard nowadays to not only provide a paycheck but also experiential learning opportunities for MU students, and we further the academic mission of the university by providing research opportunities and internships. You will see stories about these opportunities throughout this publication. This year, you will find the length of our annual report has increased. The last section of the annual report includes what formerly was a separate document – the MU Master Plan and Climate Action Plan. We have combined the documents in order to cut costs and bring the master plan and climate action plan to a broader audience. It is important to note that this is the university’s plan, and under the direction of shared governance (Campus Facilities Planning Committee, Campus Space Utilization Committee and Environmental Affairs & Sustainability Committee), though MU Operations staff help implement it. I am honored to lead MU Operations and hope you enjoy learning about our division’s accomplishments over the past year. Most of our employees work behind the scenes to provide services that make it possible for faculty, students and staff to succeed in their roles at Mizzou, and we thank you for supporting us through some difficult budgetary times