Design, implementation and evaluation of automated surveillance systems

El reconocimiento de patrones ha conseguido un nivel de complejidad que nos permite reconocer diferente tipo de eventos, incluso peligros, y actuar en concordancia para minimizar el impacto de una situación complicada y abordarla de la mejor manera posible. Sin embargo, creemos que todavía se puede llegar a alcanzar aplicaciones más eficientes con algoritmos más precisos. Nuestra aplicación quiere probar a incluir el nuevo paradigma de la programación, las redes neuronales. Nuestra idea en principio fue explorar la alternativa que las nuevas redes neuronales convolucionales aportaban, en donde se podía ver en vídeos de ejemplos la alta tasa de detección e identificación que, por ejemplo, YOLOv2 podría mostrar. Después de comparar las características, vimos que YOLOv3 ofrecía un buen balance entre precisión y rapidez como comentaremos más adelante. Debido a la tasa de baja detecciones, haremos uso de los filtros de Kalman para ayudarnos a la hora de hacer reidentificación de personas y objetos. En este proyecto, haremos un estudio además de las alternativas de videovigilancia con las que cuentan empresas del sector y veremos que clase de productos ofrecen y, por otro lado, observaremos cuales son los trabajos de los grupos de investigadores de otras universidades que más similitudes tienen con nuestro objetivo. Dedicaremos, por lo tanto, el uso de esta red neuronal para detectar eventos como el abandono de mochilas y para mostrar la densidad de tránsito en localizaciones concretas, así como utilizaremos una metodología más tradicional, el flujo óptico, para detectar actuaciones anormales en una multitud.Automatic surveillance system is getting more and more sophisticated with the increasing calculation power that computers are reaching. The aim of this project is to take advantage of these tools and with the new classification and detection technology brought by neural networks, develop a surveillance application that can recognize certain behaviours (which are the detection of lost backpacks and suitcases, detection of abnormal crowd activity and heatmap of density occupation). To develop this program, python has been the selected programming language used, where YOLO and OpenCV form the spine of this project. After testing the code, it has been proved that due to the constrains of the detection for small objects, the project does not perform as it should for real development, but still it shows potential for the detection of lost backpacks in certain videos from the GBA dataset [1] and PETS2006 dataset [2]. The abnormal activity detection for crowds is made with a simple algorithm that seems to perform well, detecting the anomalies in all the testing dataset used, generated by the University of Minnesota [3]. Finally, the heatmap can display correctly the projection of people on the ground for five second, just as intended. The objective of this software is to be part of the core of what could be a future application with more modules that will be able to perform full automated surveillance tasks and gather useful information data, and these advances and future proposal will be explained in this memory.Máster Universitario en Ingeniería Industrial (M141

Ein verallgemeinerter Prozess zur Verifikation und Validerung von Modellen und Simulationsergebnissen

With technologies increasing rapidly, symbolic, quantitative modeling and computer-based simulation (M&S) have become affordable and easy-to-apply tools in numerous application areas as, e.g., supply chain management, pilot training, car safety improvement, design of industrial buildings, or theater-level war gaming. M&S help to reduce the resources required for many types of projects, accelerate the development of technical systems, and enable the control and management of systems of high complexity. However, as the impact of M&S on the real world grows, the danger of adverse effects of erroneous or unsuitable models or simu-lation results also increases. These effects may range from the delayed delivery of an item ordered by mail to hundreds of avoidable casualties caused by the simulation-based acquisi-tion (SBA) of a malfunctioning communication system for rescue teams. In order to benefit from advancing M&S, countermeasures against M&S disadvantages and drawbacks must be taken. Verification and Validation (V&V) of models and simulation results are intended to ensure that only correct and suitable models and simulation results are used. However, during the development of any technical system including models for simulation, numerous errors may occur. The later they are detected, and the further they have propagated through the model development process, the more resources they require to correct thus, their propaga-tion should be avoided. If the errors remain undetected, and major decisions are based on in-correct or unsuitable models or simulation results, no benefit is gained from M&S, but a dis-advantage. This thesis proposes a structured and rigorous approach to support the verification and valida-tion of models and simulation results by a) the identification of the most significant of the current deficiencies of model develop-ment (design and implementation) and use, including the need for more meaningful model documentation and the lack of quality assurance (QA) as an integral part of the model development process; b) giving an overview of current quality assurance measures in M&S and in related areas. The transferability of concepts like the capability maturity model for software (SW-CMM) and the ISO9000 standard is discussed, and potentials and limits of documents such as the VV&A Recommended Practices Guide of the US Defense Modeling and Simulation Office are identified; c) analysis of quality assurance measures and so called V&V techniques for similarities and differences, to amplify their strengths and to reduce their weaknesses. d) identification and discussion of influences that drive the required rigor and intensity of V&V measures (risk involved in using models and simulation results) on the one hand, and that limit the maximum reliability of V&V activities (knowledge about both the real system and the model) on the other. This finally leads to the specification of a generalized V&V process - the V&V Triangle. It illustrates the dependencies between numerous V&V objectives, which are derived from spe-cific potential errors that occur during model development, and provides guidance for achiev-ing these objectives by the association of V&V techniques, required input, and evidence made available. The V&V Triangle is applied to an M&S sample project, and the lessons learned from evaluating the results lead to the formulation of future research objectives in M&S V&V

Real-time people tracking in a camera network

Visual tracking is a fundamental key to the recognition and analysis of human behaviour. In this thesis we present an approach to track several subjects using multiple cameras in real time. The tracking framework employs a numerical Bayesian estimator, also known as a particle lter, which has been developed for parallel implementation on a Graphics Processing Unit (GPU). In order to integrate multiple cameras into a single tracking unit we represent the human body by a parametric ellipsoid in a 3D world. The elliptical boundary can be projected rapidly, several hundred times per subject per frame, onto any image for comparison with the image data within a likelihood model. Adding variables to encode visibility and persistence into the state vector, we tackle the problems of distraction and short-period occlusion. However, subjects may also disappear for longer periods due to blind spots between cameras elds of view. To recognise a desired subject after such a long-period, we add coloured texture to the ellipsoid surface, which is learnt and retained during the tracking process. This texture signature improves the recall rate from 60% to 70-80% when compared to state only data association. Compared to a standard Central Processing Unit (CPU) implementation, there is a signi cant speed-up ratio

Simulation Exploration of the Potential of Connected Vehicles in Mitigating Secondary Crashes

Secondary crashes (SCs) on freeways are a major concern for traffic incident management systems. Studies have shown that their occurrence is significant and can lead to deterioration of traffic flow conditions on freeways in addition to injury and fatalities, albeit their magnitudes are relatively low when compared to primary crashes. Due to the limited nature of crash data in analyzing freeway SCs, surrogate measures provide an alternative for safety analysis for freeway analysis using conflict analysis. Connected Vehicles (CVs) have seen compelling technological advancements since the concept was introduced in the 1990s. In recent years, CVs have emerged as a feasible application with many safety benefits especially in the urban areas, that can be deployed in masses imminently. This study used a freeway model of a road segment in Florida’s Turnpike system in VISSIM microscopic simulation software to generate trajectory files for conflict analysis in SSAM software, to analyze potential benefits of CVs in mitigating SCs. The results showed how SCs could potentially be reduced with traffic conflicts being decreased by up to 90% at full 100% composition of CVs in the traffic stream. The results also portrayed how at only 25% CV composition, there was a significant reduction of conflicts up to 70% in low traffic volumes and up to 50% in higher traffic volumes. The statistical analysis showed that the difference in average time-to-collision surrogate measure used in deriving conflicts was significant at all levels of CV composition

Integrated Applications of Geo-Information in Environmental Monitoring

This book focuses on fundamental and applied research on geo-information technology, notably optical and radar remote sensing and algorithm improvements, and their applications in environmental monitoring. This Special Issue presents ten high-quality research papers covering up-to-date research in land cover change and desertification analyses, geo-disaster risk and damage evaluation, mining area restoration assessments, the improvement and development of algorithms, and coastal environmental monitoring and object targeting. The purpose of this Special Issue is to promote exchanges, communications and share the research outcomes of scientists worldwide and to bridge the gap between scientific research and its applications for advancing and improving society