Background Subtraction in Video Surveillance

The aim of thesis is the real-time detection of moving and unconstrained surveillance environments monitored with static cameras. This is achieved based on the results provided by background subtraction. For this task, Gaussian Mixture Models (GMMs) and Kernel density estimation (KDE) are used. A thorough review of state-of-the-art formulations for the use of GMMs and KDE in the task of background subtraction reveals some further development opportunities, which are tackled in a novel GMM-based approach incorporating a variance controlling scheme. The proposed approach method is for parametric and non-parametric and gives us the better method for background subtraction, with more accuracy and easier parametrization of the models, for different environments. It also converges to more accurate models of the scenes. The detection of moving objects is achieved by using the results of background subtraction. For the detection of new static objects, two background models, learning at different rates, are used. This allows for a multi-class pixel classification, which follows the temporality of the changes detected by means of background subtraction. In a first approach, the subtraction of background models is done for parametric model and their results are shown. The second approach is for non-parametric models, where background subtraction is done using KDE non-parametric model. Furthermore, we have done some video engineering, where the background subtraction algorithm was employed so that, the background from one video and the foreground from another video are merged to form a new video. By doing this way, we can also do more complex video engineering with multiple videos. Finally, the results provided by region analysis can be used to improve the quality of the background models, therefore, considerably improving the detection results

Robust computational intelligence techniques for visual information processing

The third part is exclusively dedicated to the super-resolution of Magnetic Resonance Images. In one of these works, an algorithm based on the random shifting technique is developed. Besides, we studied noise removal and resolution enhancement simultaneously. To end, the cost function of deep networks has been modified by different combinations of norms in order to improve their training. Finally, the general conclusions of the research are presented and discussed, as well as the possible future research lines that are able to make use of the results obtained in this Ph.D. thesis.This Ph.D. thesis is about image processing by computational intelligence techniques. Firstly, a general overview of this book is carried out, where the motivation, the hypothesis, the objectives, and the methodology employed are described. The use and analysis of different mathematical norms will be our goal. After that, state of the art focused on the applications of the image processing proposals is presented. In addition, the fundamentals of the image modalities, with particular attention to magnetic resonance, and the learning techniques used in this research, mainly based on neural networks, are summarized. To end up, the mathematical framework on which this work is based on, ₚ-norms, is defined. Three different parts associated with image processing techniques follow. The first non-introductory part of this book collects the developments which are about image segmentation. Two of them are applications for video surveillance tasks and try to model the background of a scenario using a specific camera. The other work is centered on the medical field, where the goal of segmenting diabetic wounds of a very heterogeneous dataset is addressed. The second part is focused on the optimization and implementation of new models for curve and surface fitting in two and three dimensions, respectively. The first work presents a parabola fitting algorithm based on the measurement of the distances of the interior and exterior points to the focus and the directrix. The second work changes to an ellipse shape, and it ensembles the information of multiple fitting methods. Last, the ellipsoid problem is addressed in a similar way to the parabola

Augmented Reality

Augmented Reality (AR) is a natural development from virtual reality (VR), which was developed several decades earlier. AR complements VR in many ways. Due to the advantages of the user being able to see both the real and virtual objects simultaneously, AR is far more intuitive, but it's not completely detached from human factors and other restrictions. AR doesn't consume as much time and effort in the applications because it's not required to construct the entire virtual scene and the environment. In this book, several new and emerging application areas of AR are presented and divided into three sections. The first section contains applications in outdoor and mobile AR, such as construction, restoration, security and surveillance. The second section deals with AR in medical, biological, and human bodies. The third and final section contains a number of new and useful applications in daily living and learning

Pattern Recognition

A wealth of advanced pattern recognition algorithms are emerging from the interdiscipline between technologies of effective visual features and the human-brain cognition process. Effective visual features are made possible through the rapid developments in appropriate sensor equipments, novel filter designs, and viable information processing architectures. While the understanding of human-brain cognition process broadens the way in which the computer can perform pattern recognition tasks. The present book is intended to collect representative researches around the globe focusing on low-level vision, filter design, features and image descriptors, data mining and analysis, and biologically inspired algorithms. The 27 chapters coved in this book disclose recent advances and new ideas in promoting the techniques, technology and applications of pattern recognition

Recent Trends in Computational Intelligence

Traditional models struggle to cope with complexity, noise, and the existence of a changing environment, while Computational Intelligence (CI) offers solutions to complicated problems as well as reverse problems. The main feature of CI is adaptability, spanning the fields of machine learning and computational neuroscience. CI also comprises biologically-inspired technologies such as the intellect of swarm as part of evolutionary computation and encompassing wider areas such as image processing, data collection, and natural language processing. This book aims to discuss the usage of CI for optimal solving of various applications proving its wide reach and relevance. Bounding of optimization methods and data mining strategies make a strong and reliable prediction tool for handling real-life applications

Detección de objetos en entornos dinámicos para videovigilancia

La videovigilancia por medios automáticos es un campo de investigación muy activo debido a la necesidad de seguridad y control. En este sentido, existen situaciones que dificultan el correcto funcionamiento de los algoritmos ya existentes. Esta tesis se centra en la detección de movimiento y aborda varias de las problemáticas habituales, planteando nuevos enfoques que, en la gran mayoría de las ocasiones, superan a otras propuestas pertenecientes al estado del arte. En particular estudiamos: - La importancia del espacio de color de cara a la detección de movimiento. - Los efectos del ruido en el vídeo de entrada. - Un nuevo modelo de fondo denominado MFBM que acepta cualquier número y tipo de rasgo de entrada. - Un método para paliar las dificultades que suponen los cambios de iluminación. - Un método no panorámico para detectar movimiento en cámaras no estáticas. Durante la tesis se han utilizado diferentes repositorios públicos que son ampliamente utilizados en el ámbito de la detección de movimiento. Además, los resultados obtenidos han sido comparados con los de otras propuestas existentes. Todo el código utilizado ha sido colgado en la Web de forma pública. En esta tesis se llega a las siguientes conclusiones: - El espacio de color con el que se codifique el vídeo de entrada repercute notablemente en el rendimiento de los métodos de detección. El modelo RGB no siempre es la mejor opción. También se ha comprobado que ponderar los canales de color del vídeo de entrada mejora el rendimiento de los métodos. - El ruido en el vídeo de entrada a la hora de realizar la detección de movimiento es un factor a tener en cuenta ya que condiciona el rendimiento de los métodos. Resulta llamativo que, si bien el ruido suele ser perjudicial, en ocasiones puede mejorar la detección. - El modelo MFBM supera a los demás métodos competidores estudiados, todos ellos pertenecientes al estado del arte. - Los problemas derivados de los cambios de iluminación se reducen significativamente al utilizar el método propuesto. - El método propuesto para detectar movimiento con cámaras no estáticas supera en la gran mayoría de las ocasiones a otras propuestas existentes. Se han consultado 280 entradas bibliográficas, entre ellas podemos destacar: - C. Wren, A. Azarbayejani, T. Darrell, and A. Pentl, “Pfinder: real-time tracking of the human body,” IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 19, pp. 780–785, 1997. - C. Stauffer and W. Grimson, “Adaptive background mixture models for real-time tracking,” in Proc. IEEE Intl. Conf. on Computer Vision and Pattern Recognition, 1999. - L. Li, W. Huang, I.-H. Gu, and Q. Tian, “Statistical modeling of complex backgrounds for foreground object detection,” Image Processing, IEEE Transactions on, vol. 13, pp. 1459–1472, 2004. - T. Bouwmans, “Traditional and recent approaches in background modeling for foreground detection: An overview,” Computer Science Review, vol. 11-12, pp. 31 – 66, 2014

Joint Spatial and Tonal Mosaic Alignment for Motion Detection with PTZ Camera

Scene segmentation among background and foreground (moving) regions represents the first layer of many applications such as visual surveillance. Exploiting PTZ cameras permits to widen the field of view of a surveyed area and to achieve real object tracking through pan and tilt movements of the observer point of view. Having a mosaiced background allows a system to exploit the background subtraction technique even with moving cameras. Although spatial alignment issues have been thoroughly investigated, tonal registration has been often left out of consideration. This work presents a robust general purpose technique to perform spatial and tonal image registration to achieve a background mosaic without exploiting any prior information regarding the scene or the acquisition device. Accurate experiments accomplished on outdoor and indoor scenes assess the visual quality of the mosaic. Finally, the last experiment proves the effectiveness of using such a mosaic in our visual surveillance application