Tecnicas de rastreamento e aplicações em analise cinematica de movimentos humanos

Orientador : Neucimar Jeronimo LeiteTese (doutorado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: O rastreamento de objetos a partir de imagens de video e um problema que tem despertado muito interesse nos ultimos anos, principalmente pela ampla gama de aplicaçoes, tais como realidade virtual, sistemas de vigilancia, rob'otica, analise de movimentos humanos, etc. Na analise do movimento humano as variaveis cinematicas (por exemplo, a distancia percorrida, velocidade, aceleracao, etc) sao parametros importantes para a avaliacao clinica de pessoas com deficiencias motoras, assim como para medir o rendimento do desempenho de atletas. Dentre as muitas formas de descriçao de movimentos humanos, a an'lise cinem'tica por videogrametria tem um grande potencial de aplicação dada a simplicidade dos equipamentos (cameras de video) necessarios 'a sua implementacao e 'a variedade de problemas possiveis de serem considerados. Porem, a automacao na determinacao destes parametros e um problema computacional complexo devido ao grande numero de fatores que dificultam a identificacao e extracao dos objetos de interesse. O objetivo deste trabalho 'e desenvolver algoritmos de rastreamento que permitam a obtencao automatica das coordenadas 2D ou 3D dos objetos de interesse, com a finalidade de quantificar os parametros cinematicos do movimento. Neste trabalho, os problemas relacionados ao rastreamento de objetos sao abordados a partir de duas areas de aplicacao: analise clinica (marcha, coluna, movimento respiratorio), e a 'analise de desempenho dos atletas (jogadores de futebol). No primeiro caso, apresentamos algoritmos para o rastreamento dos marcadores que sao fixados no corpo do sujeito nos pontos de interesse (por exemplo, as articulacoes). Esta abordagem leva em consideracao aspectos tais como o posicionamento e quantidade de marcadores. J'a o segundo problema aborda especificamente o rastreamento de jogadores de futebol durante uma partida, levando-se em conta as possiveis mudan¸cas de iluminacao, oclusao de jogadores e o uso de m'ultiplas cameras com diferentes enquadramentosAbstract: The problem of tracking objects from a sequence of video images has been of great interest in the recent years. This interest is motivated, especially, by the wide range of applications such as virtual reality surveillance systems, robotic, human motion analysis, etc. In the human motion analysis, the kinematic variables, (e.g., the covered distance, velocities and accelerations) are important parameters for clinical evaluation of the subjects with motion disabilities, as well as measurement of motion performance of athletes. Among the different ways of human motion description, the kinematic analysis by videogrammetry represents a wide of aplication due the simplicity of the used devices (video cameras) for their implementation and the variety of problem that can be considered. However, the automatic determination of these parameters is a complex omputational problem because of the great number of aspects that make difficult the identification and extraction of the objects of interest. The aim of this work is to develop algorithms for tracking of objects allowing an automatic determination of their 2D or 3D coordinates, as well as the quantification of the kinematic variables of human motion. In this work, the problems related to tracking are associated with two applications: the clinical analysis of human motion and athlete performance analysis. In the first case, we present algorithms for tracking of markers fixed to the human body in the regions of interest (e.g., in the articulations). This approach takes into account aspect such as placement and quantity of markers. In the second case, we consider the problem of tracking soccer players during a match, taking into consideration the possible changes of illumination, players¿ occlusion, and the use of multiple cameras covering the whole playing fieldDoutoradoCiência da ComputaçãoDoutor em Ciência da Computaçã

Rastreo de jugadores de fútbol mediante grafos multipartitos utilizando videos de ultra alta definición

Este trabajo describe un algoritmo de rastreo para jugadores de fútbol basado en gráficos multipartitos diseñados para el procesamiento de un gran volumen de datos. El algoritmo propuesto utiliza varias características como: contornos, información cromática y dinámica, para la asociación de datos dentro de un gráfico multipartito para resolver oclusiones y rastrear a jugadores de fútbol. La implementación paralela del algoritmo realiza un esquema consumidor-productor para superponer el tiempo de procesamiento de los dos procedimientos principales del algoritmo: segmentación y rastreo; así como un patrón de comunicación de envío y recepción para propagar las identidades de objetos. Mostramos cómo un sistema híbrido de paralelización de datos y tareas mejora el tiempo de ejecución para videos 4K, logrando una aceleración igual a 19.24 y una velocidad de procesamiento de 21.71 FPS con 128 subprocesos. Utilizando la base de datos ISSIA se obtuvieron valores similares de las métricas de FP y FN con una velocidad de rastreo superior.This work describes a tracking algorithm for football players based on multipartite graphs designed for the processing of high volume of data. The proposed algorithm use several characteristics such as: contours, chromatic and dinamic information, for the association of data within a multipartite graph to solve oclusions and track football player. The parallel implementation of the algorithm performce a consumer-producer scheme to overlap the computing time of the two main procedures of the tracking algorithm: segmentation and tracking; as well a send-and-receive communication pattern to propagate the blob identities. We show how an hybrid system of data and task parallelization improves the execution time for 4K videos, achieving a speedup equal to 19.24 and a processing speed of 21.71 FPS with 128 threads. Using the ISSIA database, similar values were obtained from the FP and FN metrics with a higher tracking rate.UCR::Vicerrectoría de Investigación::Sistema de Estudios de Posgrado::Ingeniería::Maestría Académica en Ingeniería Eléctric

The Analysis of Team Tactical Behaviour in Football Using GNSS Positional Data

Tactical analysis in football is an emerging field focused on assessing the collective movement of teams. Advanced player tracking technology systems facilitate the data collection for tactical analysis. GNSS tracking systems is currently the most popular player tracking technology in football application and is mainly used in physical monitoring. It also captures players positional information as geographic coordinates (i.e., latitude and longitude coordinates) which requires extra data pre-processing for tactical analysis as opposed to Cartesian coordinates (i.e., X, Y coordinates). Given the lack of a comprehensive workflow on pre-processing raw GNSS positional data for calculating tactical measures in previous publications, this thesis aimed to present a workflow that provides exemplar data, processing steps, potential issues, and corresponding solutions. With the presented workflow, not only sport scientists but also practitioners are able to engage in tactical analysis using GNSS tracking systems and bring in their own understanding and perspective. In other words, GNSS tracking systems could play an important role in both physical and tactical analysis in real-world application. Collective movements and actions vary as the match progresses along. The second objective was to use GNSS positional data to compare team tactical behaviour in different phases of a competitive match. The presented workflow was applied in data pre-processing of this analysis as a proof of concept. Although team tactical behaviour in football has been widely studied in recent years, there is no previous study that analyses team tactical behaviour in phase of attack, defence, and transition, based on tactical measures measured by positional data. In this thesis, effective playing time of a professional football match was divided into phase of in possession (IP), attack-to- defence transition (ADT), out of possession (OOP), and defence-to-attack transition (DAT). Team length, width, length per width ratio (LpW ratio), surface area, stretch indices, and interpersonal distance were calculated and compared to explore the difference of team tactical behaviour between phases. The findings showed that the team tactical behaviour during each phase was in line with the offensive and defensive tactical principles. The team presented a more dispersed and wider formation while in possession than other phases. The difference of all team tactical behaviour between IP and DAT indicated the potentiality of distinguishing defence-to-attack transition from in possession when analysing offensive tactical behaviour. Moreover, there was no significant difference across all tactical measures between defence-to-attack transition and defence, which implied that a short period of time was required for the team to switch to attack mode. In the future, the difference between transitions, attack, and defence should be valued in tactical analysis. Combining multi-type data with multi-disciplinary knowledge could inform stakeholders of dynamic team moving pattern and benefit decision making process. However, data quality (e.g., positional data and synchronisation of positional data and event data) should be prioritised in this type of study

Interactive computer vision through the Web

Computer vision is the computational science aiming at reproducing and improving the ability of human vision to understand its environment. In this thesis, we focus on two fields of computer vision, namely image segmentation and visual odometry and we show the positive impact that interactive Web applications provide on each. The first part of this thesis focuses on image annotation and segmentation. We introduce the image annotation problem and challenges it brings for large, crowdsourced datasets. Many interactions have been explored in the literature to help segmentation algorithms. The most common consist in designating contours, bounding boxes around objects, or interior and exterior scribbles. When crowdsourcing, annotation tasks are delegated to a non-expert public, sometimes on cheaper devices such as tablets. In this context, we conducted a user study showing the advantages of the outlining interaction over scribbles and bounding boxes. Another challenge of crowdsourcing is the distribution medium. While evaluating an interaction in a small user study does not require complex setup, distributing an annotation campaign to thousands of potential users might differ. Thus we describe how the Elm programming language helped us build a reliable image annotation Web application. A highlights tour of its functionalities and architecture is provided, as well as a guide on how to deploy it to crowdsourcing services such as Amazon Mechanical Turk. The application is completely opensource and available online. In the second part of this thesis we present our open-source direct visual odometry library. In that endeavor, we provide an evaluation of other open-source RGB-D camera tracking algorithms and show that our approach performs as well as the currently available alternatives. The visual odometry problem relies on geometry tools and optimization techniques traditionally requiring much processing power to perform at realtime framerates. Since we aspire to run those algorithms directly in the browser, we review past and present technologies enabling high performance computations on the Web. In particular, we detail how to target a new standard called WebAssembly from the C++ and Rust programming languages. Our library has been started from scratch in the Rust programming language, which then allowed us to easily port it to WebAssembly. Thanks to this property, we are able to showcase a visual odometry Web application with multiple types of interactions available. A timeline enables one-dimensional navigation along the video sequence. Pairs of image points can be picked on two 2D thumbnails of the image sequence to realign cameras and correct drifts. Colors are also used to identify parts of the 3D point cloud, selectable to reinitialize camera positions. Combining those interactions enables improvements on the tracking and 3D point reconstruction results