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

GbLN-PSO and Model-Based Particle Filter Approach for Tracking Human Movements in Large View Cases

Camera tracking systems have become a common requirement in today’s society. The availability of high quality and inexpensive video cameras and the increasing need for automated video analysis have generated a great deal of interest in numerous fields. Generally, it is not easy to track human behavior in an environment with a large view. This study aims to address four problems associated with large view in camera tracking system: multiple targets in nonlinear motion, relative size of the targeted object, occlusion and processing time. This paper presents a new method of tracking human movements using a GbLN-PSO and model-based particle filter to address the above problems. The proposed method has been tested with an experimental module using several sets of video data provided by the Eleventh IEEE International Workshop on Performance Evaluation of Tracking and Surveillance (PETS) and two other video streams of UBC hockey and Malaysian football games. The experiment has shown that the accuracy of tracking performance has increased up to 25% compared to others reported work in the scientific literature