Seguimiento de objetos basado en múltiples algoritmos

El objetivo principal de este trabajo consiste en analizar el seguimiento de un objeto en una secuencia de vídeo mediante la utilización de un conjunto de algoritmos de búsqueda o seguimiento. En primer lugar, se lleva a cabo un estudio inicial del seguimiento de objetos, haciendo un énfasis en los aspectos básicos de detección, búsqueda y seguimiento, así como en la combinación de los diversos algoritmos de búsqueda. Posteriormente, se procede a estudiar un conjunto de trabajos científicos (papers), que aunque se centren en el mismo tema, utilizan diferentes técnicas para lograr el objetivo deseado. Después de esta etapa de aprendizaje teórico, se empieza a utilizar la herramienta Matlab con un conjunto de algoritmos y secuencias seleccionados, comparando la eficacia de los algoritmos mediante el error basado en anotaciones manuales (groundtruth), además de compararlos entre sí con una serie de medidas que estiman la fiabilidad de los algoritmos, o comparar un algoritmo consigo mismo en instantes de tiempo diferentes. Entre dichas medidas cabe destacar la distancia entre centros y el solapamiento entre áreas detectadas en la frame correspondiente. Dichas medidas nos otorgan una capacidad de visualizar que algoritmos son óptimos en una posible combinación de estos, y cuales no. Por último, para introducir una mayor firmeza en la posible selección de los algoritmos más óptimos de cara a una combinación de estos, se utilizan mapas de confianza. Estos mapas son áreas de un tamaño similar a las frames, las cuales muestran la probabilidad de que el objeto esté en cada punto de ese mapa. Los resultados experimentales muestran las virtudes y defectos de cada algoritmo sobre el conjunto de secuencias seleccionadas utilizando las medidas anteriormente descritas. Una vez obtenidos los resultados provenientes de las medidas descritas anteriormente, se procede a realizar una fusión de algoritmos combinando aquellos con mayores puntuaciones.The main objective of this thesis consists on tracking objects in video sequences using a set of search or tracking algorithms. First, an initial study of existing works is carried out with emphasis on the basic aspects of detection, search, tracking and the combination of various search algorithms. Then, a set of scientific papers that are focused on the same topic have been studied. These papers use different techniques to achieve the desired objective. After this theoretical learning stage, the Matlab tool is used to test a set of selected algorithms and sequences, comparing the effectiveness of the algorithms by the error based on ground-truth also to compare them with a series of measures, or comparing an algorithm himself in different time instants to estimate their reliability. These measures employ the distance between centers and the overlap between areas detected in the corresponding frame. These measures give us an ability to visualize which algorithms are optimal in a possible combination of these, and which are not. Finally, confidence maps are used to estimate results’ stabilitiy in the possible selection of the optimal algorithms for their combination. These maps are extracted from each algorithm and represent areas of a size similar to the frames which show the probability that the object is located at each point of the map. Experimental results show the strengths and weaknesses of each algorithm on the set of selected sequences using the proposed set of reliability measures. Once the results obtained from the measures described above, we proceed to combine the best algorithms as proposed

Metropolitan intelligent surveillance systems for urban areas by harnessing IoT and edge computing paradigms

Copyright © 2018 John Wiley & Sons, Ltd. Recent technological advances led to the rapid and uncontrolled proliferation of intelligent surveillance systems (ISSs), serving to supervise urban areas. Driven by pressing public safety and security requirements, modern cities are being transformed into tangled cyber-physical environments, consisting of numerous heterogeneous ISSs under different administrative domains with low or no capabilities for reuse and interaction. This isolated pattern renders itself unsustainable in city-wide scenarios that typically require to aggregate, manage, and process multiple video streams continuously generated by distributed ISS sources. A coordinated approach is therefore required to enable an interoperable ISS for metropolitan areas, facilitating technological sustainability to prevent network bandwidth saturation. To meet these requirements, this paper combines several approaches and technologies, namely the Internet of Things, cloud computing, edge computing and big data, into a common framework to enable a unified approach to implementing an ISS at an urban scale, thus paving the way for the metropolitan intelligent surveillance system (MISS). The proposed solution aims to push data management and processing tasks as close to data sources as possible, thus increasing performance and security levels that are usually critical to surveillance systems. To demonstrate the feasibility and the effectiveness of this approach, the paper presents a case study based on a distributed ISS scenario in a crowded urban area, implemented on clustered edge devices that are able to off-load tasks in a “horizontal” manner in the context of the developed MISS framework. As demonstrated by the initial experiments, the MISS prototype is able to obtain face recognition results 8 times faster compared with the traditional off-loading pattern, where processing tasks are pushed “vertically” to the cloud

Applications of a Graph Theoretic Based Clustering Framework in Computer Vision and Pattern Recognition

Recently, several clustering algorithms have been used to solve variety of problems from different discipline. This dissertation aims to address different challenging tasks in computer vision and pattern recognition by casting the problems as a clustering problem. We proposed novel approaches to solve multi-target tracking, visual geo-localization and outlier detection problems using a unified underlining clustering framework, i.e., dominant set clustering and its extensions, and presented a superior result over several state-of-the-art approaches.Comment: doctoral dissertatio

Getting their acts together: A coordinated systems approach to extended cognition

A cognitive system is a set of processes responsible for intelligent behaviour. This thesis is an attempt to answer the question: how can cognitive systems be demarcated; that is, what criterion can be used to decide where to draw the boundary of the system? This question is important because it is one way of couching the hypothesis of extended cognition – is it possible for cognitive systems to transcend the boundary of the brain or body of an organism? Such a criterion can be supplied by what is called in the literature a ‘mark of the cognitive’. The main task of this thesis is to develop a general mark of the cognitive. The starting point is that a system responsible for intelligent behaviour is a coordinated coalition of processes. This account proposes a set of functional conditions for coordination. These conditions can then be used as a sufficient condition for membership of a cognitive system. In certain circumstances, they assert that a given process plays a coordination role in the system and is therefore part of the system. The controversy in the extended cognition debate surrounds positive claims of systemhood concerning ‘external’ processes so a sufficient condition will help settle some of these debates. I argue that a Coordinated Systems Approach like this will help to move the extended cognition debate forward from its current impasse. Moreover, the application of the approach to social systems and stygmergic systems - systems where current processes are coordinated partly by the trace of previous action – promises new directions for research

Gamma-Ray Transients observed with the Fermi Large Area Telescope

Many observed phenomena in the universe are not static: this is why time- domain astrophysics is a key field of current astronomy and astrophysics. The temporal domain offers an important window on the understanding of extreme phases of stellar and galaxy evolution through studies of novae, supernovae, Gamma-Ray Bursts (GRBs), pulsars, Active Galactic Nuclei (AGN) to list only a few. Variable objects show some different characteristic variability and a lot of information on the physical processes at work comes from measuring luminosity and spectral variations over time. Launched in June 2008, during its first eight years of operation the Fermi Gamma-Ray Space Telescope has confirmed that the gamma-ray sky is highly dynamic on all time scales, from \u3bcs to years, providing insight into extreme physical conditions. My research activity is focused on the analysis of transient gamma-ray sources observed by the Large Area Telescope (LAT), the main instrument on-board Fermi. The variable sources analyzed in this thesis spans different observing time scales: - GRBs and solar flares show an impulsive and short phase that can last from 3c ms to some hundreds of seconds, and a time-extended phase observed at higher energies that can lasts several hours;\u2028- AGN show variability from hours to days;\u2028- novae, whose high-energy transient emission lasts for weeks. In chapters number 2 and 3, I will present the spectral analysis of the impulsive phase of gamma-ray bursts and solar flares using data from the LAT and the GBM. The goal of of this work has been to develop a semi-automatic analysis-pipeline to optimize the source selection and the modelling of background emission in order to better constrain spectral features and infer important physical information on emission processes at work. In chapter 4 and 5, I will show the analysis performed on LAT data for AGN and novae, in the context of coordinated very-high energy ( 3c TeV) observations with the Major Atmospheric Gamma Imaging Cherenkov (MAGIC) telescopes. The aim of Chapter 1 is to present the main features of the observatories whose data have been used for my analyses

A Comprehensive Review on Computer Vision Analysis of Aerial Data

With the emergence of new technologies in the field of airborne platforms and imaging sensors, aerial data analysis is becoming very popular, capitalizing on its advantages over land data. This paper presents a comprehensive review of the computer vision tasks within the domain of aerial data analysis. While addressing fundamental aspects such as object detection and tracking, the primary focus is on pivotal tasks like change detection, object segmentation, and scene-level analysis. The paper provides the comparison of various hyper parameters employed across diverse architectures and tasks. A substantial section is dedicated to an in-depth discussion on libraries, their categorization, and their relevance to different domain expertise. The paper encompasses aerial datasets, the architectural nuances adopted, and the evaluation metrics associated with all the tasks in aerial data analysis. Applications of computer vision tasks in aerial data across different domains are explored, with case studies providing further insights. The paper thoroughly examines the challenges inherent in aerial data analysis, offering practical solutions. Additionally, unresolved issues of significance are identified, paving the way for future research directions in the field of aerial data analysis.Comment: 112 page