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• W2168774818 abstract "Esta tesis propone varios objetivos interconectados para el diseno de un sistema de videovigilancia cuyo funcionamiento es pensado para un amplio rango de condiciones. Primeramente se propone una metrica de evaluacion del detector y sistema de seguimiento basada en una minima referencia. Dicha tecnica es una respuesta a la demanda de ajuste de forma rapida y facil del sistema adecuandose a distintos entornos. Tambien se propone una tecnica de optimizacion basada en Estrategias Evolutivas y la combinacion de funciones de idoneidad en varios pasos. El objetivo es obtener los parametros de ajuste del detector y el sistema de seguimiento adecuados para el mejor funcionamiento en una amplia gama de situaciones posibles Finalmente, se propone la construccion de un clasificador basado en tecnicas no parametricas que pudieran modelar la distribucion de datos de entrada independientemente de la fuente de generacion de dichos datos. Se escogen actividades detectables a corto plazo que siguen un patron de tiempo que puede ser facilmente modelado mediante HMMs. La propuesta consiste en una modificacion del algoritmo de Baum-Welch con el fin de modelar las probabilidades de emision del HMM mediante una tecnica no parametrica basada en estimacion de densidad con kernels (KDE). _____________________________________This thesis proposes several interconnected objectives for the design of a video-monitoring system whose operation is thought for a wide rank of conditions. Firstly an evaluation technique of the detector and tracking system is proposed and it is based on a minimum reference or ground-truth. This technique is an answer to the demand of fast and easy adjustment of the system adapting itself to different contexts. Also, this thesis proposes a technique of optimization based on Evolutionary Strategies and the combination of fitness functions. The objective is to obtain the parameters of adjustment of the detector and tracking system for the best operation in an ample range of possible situations. Finally, it is proposed the generation of a classifier in which a non-parametric statistic technique models the distribution of data regardless the source generation of such data. Short term detectable activities are chosen that follow a time pattern that can easily be modeled by Hidden Markov Models (HMMs). The proposal consists in a modification of the Baum-Welch algorithm with the purpose of modeling the emission probabilities of the HMM by means of a nonparametric technique based on the density estimation with kernels (KDE)." @default.
• W2168774818 created "2016-06-24" @default.
• W2168774818 creator A5050336723 @default.
• W2168774818 date "2008-03-01" @default.
• W2168774818 modified "2023-09-24" @default.
• W2168774818 title "Soft computing and non-parametric techniques for effective video surveillance systems" @default.
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