Sistema de visión sinérgico para detección de movimiento

La detección de movimiento en sistemas de vigilancia y monitoreo se ve favorecida por la combinación sinérgica de diferentes tipos de cámaras y su óptima distribución sobre el área de interés. Se propone un modelo de optimización para un sistema de visión sinérgico basado en programación lineal entera. Los objetivos son encontrar la posición y orientación óptima de cada una de las cámaras direccionales y omnidireccionales con el fin de maximizar la cobertura del espacio de trabajo y detectar los objetos en movimiento presentes. Para detectar eficientemente el movimiento, incluso ante cambios de luminosidad globales, se utiliza un algoritmo de substracción de fondo que usa la información espacial de la textura. El método propuesto se evalúa en un conjunto representativo de escenarios reales utilizando una red de cámaras. Los resultados muestran que nuestro algoritmo es capaz de determinar el número mínimo de cámaras necesario para cubrir un área determinada

A Distributed Optimal Control Approach for Multi-agent Trajectory Optimization

<p>This dissertation presents a novel distributed optimal control (DOC) problem formulation that is applicable to multiscale dynamical systems comprised of numerous interacting systems, or agents, that together give rise to coherent macroscopic behaviors, or coarse dynamics, that can be modeled by partial differential equations (PDEs) on larger spatial and time scales. The DOC methodology seeks to obtain optimal agent state and control trajectories by representing the system's performance as an integral cost function of the macroscopic state, which is optimized subject to the agents' dynamics. The macroscopic state is identified as a time-varying probability density function to which the states of the individual agents can be mapped via a restriction operator. Optimality conditions for the DOC problem are derived analytically, and the optimal trajectories of the macroscopic state and control are computed using direct and indirect optimization algorithms. Feedback microscopic control laws are then derived from the optimal macroscopic description using a potential function approach.</p><p>The DOC approach is demonstrated numerically through benchmark multi-agent trajectory optimization problems, where large systems of agents were given the objectives of traveling to goal state distributions, avoiding obstacles, maintaining formations, and minimizing energy consumption through control. Comparisons are provided between the direct and indirect optimization techniques, as well as existing methods from the literature, and a computational complexity analysis is presented. The methodology is also applied to a track coverage optimization problem for the control of distributed networks of mobile omnidirectional sensors, where the sensors move to maximize the probability of track detection of a known distribution of mobile targets traversing a region of interest (ROI). Through extensive simulations, DOC is shown to outperform several existing sensor deployment and control strategies. Furthermore, the computation required by the DOC algorithm is proven to be far reduced compared to that of classical, direct optimal control algorithms.</p>Dissertatio