Detección de personas en presencia de grupos

La visión por computador es una rama de la informática, donde se trata de dotar a las máquinas de ese estimable sentido que es la vista. Esto unido a la inteligencia artificial, donde se quiere conseguir que un computador tenga consciencia, puede lograr que de manera automática y autónoma un ordenador pueda, por ejemplo, vigilar la seguridad de las personas. Por otro lado, la vídeo-vigilancia puede procesar diferentes tipos de entradas para generar alertas que un usuario pueda utilizar para evitar un insatisfactorio suceso. No hace falta decir que la vídeo-vigilancia no es nada sin la visión artificial, la cual es la encargada de hacer todo el procesamiento de las entradas que proporcionan las cámaras, y dar esas salidas que son las detecciones deseadas. Este proyecto consta de, por lo tanto, una unión de ambas. Debe de conseguir, utilizando diferentes herramientas, la transparente unificación de todos estos conceptos. Para ello, se quiere como objetivo final conseguir detectar personas de una manera eficaz cuando estas están en grupos de mayor o menor dimensión. Concretando, buscamos una mejora en la detección de personas en presencia de grupos. En este tipo de escenarios es aún más compleja la detección, dado que las personas pueden están altamente ocluidas unas con otras. Este proyecto lo podríamos catalogar como I+D+i. Dado que se investiga todo lo relacionado con la visión artificial y la vídeo-vigilancia. Se desarrolla un software a modo de prototipo que dispone de todas las funcionalidades deseadas. Y también se innova en la implementación propia de diferentes algoritmos. Por último, es importante recalcar que la principal vocación del presente trabajo es sentar las bases para el desarrollo posterior de diferentes aplicaciones relacionadas con la monitorización automática de escenas tanto desde el punto de vista de la implementación eficiente como desde el punto de vista del diseño de nuevos algoritmos de detección.Computer vision is a field in computer science, which tries to provide machines with that appreciable sense that is sight. This coupled with artificial intelligence, where you want a computer to have consciousness, can make automatically and autonomously that computers, for example, monitor people safety. Furthermore, video surveillance can process different types of inputs to generate alerts that a user can use to avoid an unsatisfactory event. Needless to say that video surveillance is nothing without the artificial vision, which is responsible for all of the processing of the inputs, which the cameras provide, and give those outputs, which are the desire detections. This project involves, therefore, a junction of the two issues. It must achieve, using different tools, the transparent unification of all these concepts. Therefore, our final aim is to detect people effectively in crowd scenes. Specifying, we search an improvement in the people detection in the presence of the groups. In this type of scenario is even more complex detection, since people can are highly occluded with other persons. This project could be included as a I+D+i type. Since everything related to computer vision and video surveillance is investigated. Specific software is developed as a prototype that has all the desired features. And also innovates in the actual implementation of different algorithms. Finally, make clear that what we want is to be helpful to different projects that linked to this one, can increase knowledge, effectiveness and efficiency of different investigations and future implementations. Finally, it is important to stress that the main vocation of the present work is to as much lay the foundations for the later development of different applications related to the automatic monitoring from scenes from the point of view of the efficient implementation as from the point of view of the design of new algorithms of detection

Bioinspired metaheuristic algorithms for global optimization

This paper presents concise comparison study of newly developed bioinspired algorithms for global optimization problems. Three different metaheuristic techniques, namely Accelerated Particle Swarm Optimization (APSO), Firefly Algorithm (FA), and Grey Wolf Optimizer (GWO) are investigated and implemented in Matlab environment. These methods are compared on four unimodal and multimodal nonlinear functions in order to find global optimum values. Computational results indicate that GWO outperforms other intelligent techniques, and that all aforementioned algorithms can be successfully used for optimization of continuous functions

Experimental Evaluation of Growing and Pruning Hyper Basis Function Neural Networks Trained with Extended Information Filter

In this paper we test Extended Information Filter (EIF) for sequential training of Hyper Basis Function Neural Networks with growing and pruning ability (HBF-GP). The HBF neuron allows different scaling of input dimensions to provide better generalization property when dealing with complex nonlinear problems in engineering practice. The main intuition behind HBF is in generalization of Gaussian type of neuron that applies Mahalanobis-like distance as a distance metrics between input training sample and prototype vector. We exploit concept of neuron’s significance and allow growing and pruning of HBF neurons during sequential learning process. From engineer’s perspective, EIF is attractive for training of neural networks because it allows a designer to have scarce initial knowledge of the system/problem. Extensive experimental study shows that HBF neural network trained with EIF achieves same prediction error and compactness of network topology when compared to EKF, but without the need to know initial state uncertainty, which is its main advantage over EKF

XXIII Congreso Argentino de Ciencias de la Computación - CACIC 2017 : Libro de actas

Trabajos presentados en el XXIII Congreso Argentino de Ciencias de la Computación (CACIC), celebrado en la ciudad de La Plata los días 9 al 13 de octubre de 2017, organizado por la Red de Universidades con Carreras en Informática (RedUNCI) y la Facultad de Informática de la Universidad Nacional de La Plata (UNLP).Red de Universidades con Carreras en Informática (RedUNCI

Iowa State University, Courses and Programs Catalog 2014–2015

The Iowa State University Catalog is a one-year publication which lists all academic policies, and procedures. The catalog also includes the following: information for fees; curriculum requirements; first-year courses of study for over 100 undergraduate majors; course descriptions for nearly 5000 undergraduate and graduate courses; and a listing of faculty members at Iowa State University.https://lib.dr.iastate.edu/catalog/1025/thumbnail.jp