Motion Segmentation Aided Super Resolution Image Reconstruction

This dissertation addresses Super Resolution (SR) Image Reconstruction focusing on motion segmentation. The main thrust is Information Complexity guided Gaussian Mixture Models (GMMs) for Statistical Background Modeling. In the process of developing our framework we also focus on two other topics; motion trajectories estimation toward global and local scene change detections and image reconstruction to have high resolution (HR) representations of the moving regions. Such a framework is used for dynamic scene understanding and recognition of individuals and threats with the help of the image sequences recorded with either stationary or non-stationary camera systems. We introduce a new technique called Information Complexity guided Statistical Background Modeling. Thus, we successfully employ GMMs, which are optimal with respect to information complexity criteria. Moving objects are segmented out through background subtraction which utilizes the computed background model. This technique produces superior results to competing background modeling strategies. The state-of-the-art SR Image Reconstruction studies combine the information from a set of unremarkably different low resolution (LR) images of static scene to construct an HR representation. The crucial challenge not handled in these studies is accumulating the corresponding information from highly displaced moving objects. In this aspect, a framework of SR Image Reconstruction of the moving objects with such high level of displacements is developed. Our assumption is that LR images are different from each other due to local motion of the objects and the global motion of the scene imposed by non-stationary imaging system. Contrary to traditional SR approaches, we employed several steps. These steps are; the suppression of the global motion, motion segmentation accompanied by background subtraction to extract moving objects, suppression of the local motion of the segmented out regions, and super-resolving accumulated information coming from moving objects rather than the whole scene. This results in a reliable offline SR Image Reconstruction tool which handles several types of dynamic scene changes, compensates the impacts of camera systems, and provides data redundancy through removing the background. The framework proved to be superior to the state-of-the-art algorithms which put no significant effort toward dynamic scene representation of non-stationary camera systems

Segmentación persona-fondo usando información de segmentación frente-fondo

En la actualidad el procesamiento y análisis de vídeo está en auge, esto es debido principalmente a la masiva instalación de cámaras de video para múltiples cometidos. Por este motivo, la algoritmia relativa al procesamiento y análisis de vídeo ha adquirido suma importancia. Actualmente los algoritmos de segmentación Persona- Fondo no están muy extendidos, por el contrario los segmentadores Frente-Fondo han sido desarrollados mucho más que los primeros. Por este motivo, el objetivo principal de este proyecto es mejorar la segmentación Persona-Fondo usando información de segmentación Frente-Fondo puesto que ambas segmentaciones tienen objetivos distintos. Por eso tras un amplio análisis del Estado del Arte, en el caso de la segmentación Persona-Fondo el algoritmo disponible en el laboratorio VPULab, se estudió el funcionamiento de los segmentadores elegidos en ambos casos. Tras el estudio, se desarrollaron diversas opciones de unir las informaciones de ambas segmentaciones para mejorar en primer lugar la segmentación Persona-Fondo. Una vez se obtuvo un método óptimo, se aplicó el mismo a la segmentaci ón Frente-Fondo para obtener también una mejora de la misma. Una vez se obtuvieron los métodos óptimos, se procedió a la evaluación de las segmentaciones por independiente, posteriormente se evaluó un primer método sin obtener resultados óptimos. Tras esto, se evaluaron los dos métodos óptimos desarrollados en el proyecto usando un dataset conformado por vídeos del Estado del Arte, obteniendo resultados satisfactorios de ambos métodos.Nowadays the video processing and analysis is in full developing, this is mainly due to the massive installation of video cameras for multiple tasks. For this reason, the algorithms of the video processing and analysis have gained importance. Currently People-Background segmentation algorithms are not spread out, however the Background-Foreground segmentation has been developed more than the rsts one. Therefore, the main objective of this project is to enhance the People-Background segmentation using Background-Foreground segmentation information as both segmentations have di erent objectives. So after an extensive analysis of the State of Art, in the case of People-Background segmentation the algorithm available in the VPULab, the functioning of the segmentation was studied in both cases. After that, various options of attaching the information to improve the People-Background segmentation was developed. Once an optimal method was obtained, it was applied to the Foreground-Background segmentation to obtain also an improvement. Once the optimal methods were obtained, an evaluation was made for both kinds of segmentation independently. Later one of the method was evaluated obtaining not enough improvement. After this, both optimal methods were evaluated with a dataset made by videos of the State of Art, obtaining good results

A Methodology for Extracting Human Bodies from Still Images

Monitoring and surveillance of humans is one of the most prominent applications of today and it is expected to be part of many future aspects of our life, for safety reasons, assisted living and many others. Many efforts have been made towards automatic and robust solutions, but the general problem is very challenging and remains still open. In this PhD dissertation we examine the problem from many perspectives. First, we study the performance of a hardware architecture designed for large-scale surveillance systems. Then, we focus on the general problem of human activity recognition, present an extensive survey of methodologies that deal with this subject and propose a maturity metric to evaluate them. One of the numerous and most popular algorithms for image processing found in the field is image segmentation and we propose a blind metric to evaluate their results regarding the activity at local regions. Finally, we propose a fully automatic system for segmenting and extracting human bodies from challenging single images, which is the main contribution of the dissertation. Our methodology is a novel bottom-up approach relying mostly on anthropometric constraints and is facilitated by our research in the fields of face, skin and hands detection. Experimental results and comparison with state-of-the-art methodologies demonstrate the success of our approach

A Literature Study On Video Retrieval Approaches

A detailed survey has been carried out to identify the various research articles available in the literature in all the categories of video retrieval and to do the analysis of the major contributions and their advantages, following are the literature used for the assessment of the state-of-art work on video retrieval. Here, a large number of papershave been studied