59 research outputs found
A Bayesian network for combining descriptors: application to symbol recognition
International audienceIn this paper, we propose a descriptor combination method, which enables to improve significantly the recognition rate compared to the recognition rates obtained by each descriptor. This approach is based on a probabilistic graphical model. This model also enables to handle both discrete and continuous-valued variables. In fact, in order to improve the recognition rate, we have combined two kinds of features: discrete features (corresponding to shapes measures) and continuous features (corresponding to shape descriptors). In order to solve the dimensionality problem due to the large dimension of visual features, we have adapted a variable selection method. Experimental results, obtained in a supervised learning context, on noisy and occluded symbols, show the feasibility of the approach
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
Fish4Knowledge: Collecting and Analyzing Massive Coral Reef Fish Video Data
This book gives a start-to-finish overview of the whole Fish4Knowledge project, in 18 short chapters, each describing one aspect of the project. The Fish4Knowledge project explored the possibilities of big video data, in this case from undersea video. Recording and analyzing 90 thousand hours of video from ten camera locations, the project gives a 3 year view of fish abundance in several tropical coral reefs off the coast of Taiwan. The research system built a remote recording network, over 100 Tb of storage, supercomputer processing, video target detection and
Using computer vision to categorize tyres and estimate the number of visible tyres in tyre stockpile images
Pressures from environmental agencies contribute to the challenges associated with the disposal of waste tyres, particularly in South Africa. Recycling of waste tyres in South Africa is in its infancy resulting in the historically undocumented and uncontrolled existence of waste tyre stockpiles across the country. The remote and distant locations of such stockpiles typically complicate the logistics associated with the collection, transport and storage of waste tyres prior to entering the recycling process. In order to optimize the logistics associated with the collection of waste tyres from stockpiles, useful information about such stockpiles would include estimates of the types of tyres as well as the quantity of specific tyre types found in particular stockpiles. This research proposes the use of computer vision for categorizing individual tyres and estimating the number of visible tyres in tyre stockpile images to support the logistics in tyre recycling efforts. The study begins with a broad review of image processing and computer vision algorithms for categorization and counting objects in images. The bag of visual words (BoVW) model for categorization is tested on two small data sets of tread tyre images using a random sub-sampling holdout method. The categorization results are evaluated using performance metrics for multiclass classifiers, namely the average accuracy, precision, and recall. The results indicated that corner-based local feature detectors combined with speeded up robust features (SURF) descriptors in a BoVW model provide moderately accurate categorization of tyres based on tread images. Two feature extraction methods for extracting features for use in training neural networks (NNs) for tyre count estimations in tyre stockpiles are proposed. The two feature extraction methods are used to describe images in terms of feature vectors that can be used as input for NNs. The first feature extraction method uses the BoVW model with histograms of oriented gradients (HOG) features collected from overlapping sub-images to create a visual vocabulary and describe the images in terms of their visual word occurrence histogram. The second feature extraction method uses the image gradient magnitude, gradient orientation, and edge orientations of edges detected using the Canny edge detector. A concatenated histogram is constructed from individual histograms of gradient orientations and gradient magnitude. The histograms are then used to train NNs using backpropogation to approximate functions from the feature vectors describing the images to scalar count estimations. The accuracy of visible object count predictions are evaluated using NN evaluation techniques to determine the accuracy of predictions and the generalization ability of the fit model. The count estimation experiments using the two feature extraction methods for input to NNs showed that fairly accurate count estimations can be obtained and that the fit model could generalize fairly well to unseen images
Mathematical Expression Recognition based on Probabilistic Grammars
[EN] Mathematical notation is well-known and used all over the
world. Humankind has evolved from simple methods representing
countings to current well-defined math notation able to account for
complex problems. Furthermore, mathematical expressions constitute a
universal language in scientific fields, and many information
resources containing mathematics have been created during the last
decades. However, in order to efficiently access all that information,
scientific documents have to be digitized or produced directly in
electronic formats.
Although most people is able to understand and produce mathematical
information, introducing math expressions into electronic devices
requires learning specific notations or using editors. Automatic
recognition of mathematical expressions aims at filling this gap
between the knowledge of a person and the input accepted by
computers. This way, printed documents containing math expressions
could be automatically digitized, and handwriting could be used for
direct input of math notation into electronic devices.
This thesis is devoted to develop an approach for mathematical
expression recognition. In this document we propose an approach for
recognizing any type of mathematical expression (printed or
handwritten) based on probabilistic grammars. In order to do so, we
develop the formal statistical framework such that derives several
probability distributions. Along the document, we deal with the
definition and estimation of all these probabilistic sources of
information. Finally, we define the parsing algorithm that globally
computes the most probable mathematical expression for a given input
according to the statistical framework.
An important point in this study is to provide objective performance
evaluation and report results using public data and standard
metrics. We inspected the problems of automatic evaluation in this
field and looked for the best solutions. We also report several
experiments using public databases and we participated in several
international competitions. Furthermore, we have released most of the
software developed in this thesis as open source.
We also explore some of the applications of mathematical expression
recognition. In addition to the direct applications of transcription
and digitization, we report two important proposals. First, we
developed mucaptcha, a method to tell humans and computers apart by
means of math handwriting input, which represents a novel application
of math expression recognition. Second, we tackled the problem of
layout analysis of structured documents using the statistical
framework developed in this thesis, because both are two-dimensional
problems that can be modeled with probabilistic grammars.
The approach developed in this thesis for mathematical expression
recognition has obtained good results at different levels. It has
produced several scientific publications in international conferences
and journals, and has been awarded in international competitions.[ES] La notación matemática es bien conocida y se utiliza en todo el
mundo. La humanidad ha evolucionado desde simples métodos para
representar cuentas hasta la notación formal actual capaz de modelar
problemas complejos. Además, las expresiones matemáticas constituyen
un idioma universal en el mundo científico, y se han creado muchos
recursos que contienen matemáticas durante las últimas décadas. Sin
embargo, para acceder de forma eficiente a toda esa información, los
documentos científicos han de ser digitalizados o producidos
directamente en formatos electrónicos.
Aunque la mayoría de personas es capaz de entender y producir
información matemática, introducir expresiones matemáticas en
dispositivos electrónicos requiere aprender notaciones especiales o
usar editores. El reconocimiento automático de expresiones matemáticas
tiene como objetivo llenar ese espacio existente entre el conocimiento
de una persona y la entrada que aceptan los ordenadores. De este modo,
documentos impresos que contienen fórmulas podrían digitalizarse
automáticamente, y la escritura se podría utilizar para introducir
directamente notación matemática en dispositivos electrónicos.
Esta tesis está centrada en desarrollar un método para reconocer
expresiones matemáticas. En este documento proponemos un método para
reconocer cualquier tipo de fórmula (impresa o manuscrita) basado en
gramáticas probabilísticas. Para ello, desarrollamos el marco
estadístico formal que deriva varias distribuciones de probabilidad. A
lo largo del documento, abordamos la definición y estimación de todas
estas fuentes de información probabilística. Finalmente, definimos el
algoritmo que, dada cierta entrada, calcula globalmente la expresión
matemática más probable de acuerdo al marco estadístico.
Un aspecto importante de este trabajo es proporcionar una evaluación
objetiva de los resultados y presentarlos usando datos públicos y
medidas estándar. Por ello, estudiamos los problemas de la evaluación
automática en este campo y buscamos las mejores soluciones. Asimismo,
presentamos diversos experimentos usando bases de datos públicas y
hemos participado en varias competiciones internacionales. Además,
hemos publicado como código abierto la mayoría del software
desarrollado en esta tesis.
También hemos explorado algunas de las aplicaciones del reconocimiento
de expresiones matemáticas. Además de las aplicaciones directas de
transcripción y digitalización, presentamos dos propuestas
importantes. En primer lugar, desarrollamos mucaptcha, un método para
discriminar entre humanos y ordenadores mediante la escritura de
expresiones matemáticas, el cual representa una novedosa aplicación
del reconocimiento de fórmulas. En segundo lugar, abordamos el
problema de detectar y segmentar la estructura de documentos
utilizando el marco estadístico formal desarrollado en esta tesis,
dado que ambos son problemas bidimensionales que pueden modelarse con
gramáticas probabilísticas.
El método desarrollado en esta tesis para reconocer expresiones
matemáticas ha obtenido buenos resultados a diferentes niveles. Este
trabajo ha producido varias publicaciones en conferencias
internacionales y revistas, y ha sido premiado en competiciones
internacionales.[CA] La notació matemàtica és ben coneguda i s'utilitza a tot el món. La
humanitat ha evolucionat des de simples mètodes per representar
comptes fins a la notació formal actual capaç de modelar
problemes complexos. A més, les expressions matemàtiques
constitueixen un idioma universal al món científic, i s'han creat
molts recursos que contenen matemàtiques durant les últimes
dècades. No obstant això, per accedir de forma eficient a tota
aquesta informació, els documents científics han de ser
digitalitzats o produïts directament en formats electrònics.
Encara que la majoria de persones és capaç d'entendre i produir
informació matemàtica, introduir expressions matemàtiques en
dispositius electrònics requereix aprendre notacions especials o usar
editors. El reconeixement automàtic d'expressions matemàtiques
té per objectiu omplir aquest espai existent entre el coneixement
d'una persona i l'entrada que accepten els ordinadors. D'aquesta
manera, documents impresos que contenen fórmules podrien
digitalitzar-se automàticament, i l'escriptura es podria utilitzar per
introduir directament notació matemàtica en dispositius electrònics.
Aquesta tesi està centrada en desenvolupar un mètode per reconèixer
expressions matemàtiques. En aquest document proposem un mètode per
reconèixer qualsevol tipus de fórmula (impresa o manuscrita) basat en
gramàtiques probabilístiques. Amb aquesta finalitat, desenvolupem el
marc estadístic formal que deriva diverses distribucions de
probabilitat. Al llarg del document, abordem la definició i estimació
de totes aquestes fonts d'informació probabilística. Finalment,
definim l'algorisme que, donada certa entrada, calcula globalment
l'expressió matemàtica més probable d'acord al marc estadístic.
Un aspecte important d'aquest treball és proporcionar una avaluació
objectiva dels resultats i presentar-los usant dades públiques i
mesures estàndard. Per això, estudiem els problemes de l'avaluació
automàtica en aquest camp i busquem les millors solucions. Així
mateix, presentem diversos experiments usant bases de dades públiques
i hem participat en diverses competicions internacionals. A més, hem
publicat com a codi obert la majoria del software desenvolupat en
aquesta tesi.
També hem explorat algunes de les aplicacions del reconeixement
d'expressions matemàtiques. A més de les aplicacions directes de
transcripció i digitalització, presentem dues propostes
importants. En primer lloc, desenvolupem mucaptcha, un mètode per
discriminar entre humans i ordinadors mitjançant l'escriptura
d'expressions matemàtiques, el qual representa una nova aplicació del
reconeixement de fórmules. En segon lloc, abordem el problema de
detectar i segmentar l'estructura de documents utilitzant el marc
estadístic formal desenvolupat en aquesta tesi, donat que ambdós són
problemes bidimensionals que poden modelar-se amb gramàtiques
probabilístiques.
El mètode desenvolupat en aquesta tesi per reconèixer expressions
matemàtiques ha obtingut bons resultats a diferents nivells. Aquest
treball ha produït diverses publicacions en conferències
internacionals i revistes, i ha sigut premiat en competicions
internacionals.Álvaro Muñoz, F. (2015). Mathematical Expression Recognition based on Probabilistic Grammars [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/51665TESI
Learning error-correcting representations for multi-class problems
[eng] Real life is full of multi-class decision tasks. In the Pattern Recognition field, several method- ologies have been proposed to deal with binary problems obtaining satisfying results in terms of performance. However, the extension of very powerful binary classifiers to the multi-class case is a complex task. The Error-Correcting Output Codes framework has demonstrated to be a very powerful tool to combine binary classifiers to tackle multi-class problems. However, most of the combinations of binary classifiers in the ECOC framework overlook the underlay- ing structure of the multi-class problem. In addition, is still unclear how the Error-Correction of an ECOC design is distributed among the different classes. In this dissertation, we are interested in tackling critic problems of the ECOC framework, such as the definition of the number of classifiers to tackle a multi-class problem, how to adapt the ECOC coding to multi-class data and how to distribute error-correction among different pairs of categories. In order to deal with this issues, this dissertation describes several proposals. 1) We define a new representation for ECOC coding matrices that expresses the pair-wise codeword separability and allows for a deeper understanding of how error-correction is distributed among classes. 2) We study the effect of using a logarithmic number of binary classifiers to treat the multi-class problem in order to obtain very efficient models. 3) In order to search for very compact ECOC coding matrices that take into account the distribution of multi-class data we use Genetic Algorithms that take into account the constraints of the ECOC framework. 4) We propose a discrete factorization algorithm that finds an ECOC configuration that allocates the error-correcting capabilities to those classes that are more prone to errors. The proposed methodologies are evaluated on different real and synthetic data sets: UCI Machine Learning Repository, handwriting symbols, traffic signs from a Mobile Mapping System, and Human Pose Recovery. The results of this thesis show that significant perfor- mance improvements are obtained on traditional coding ECOC designs when the proposed ECOC coding designs are taken into account. [[spa] En la vida cotidiana las tareas de decisión multi-clase surgen constantemente. En el campo de Reconocimiento de Patrones muchos métodos de clasificación binaria han sido propuestos obteniendo resultados altamente satisfactorios en términos de rendimiento. Sin embargo, la extensión de estos sofisticados clasificadores binarios al contexto multi-clase es una tarea compleja. En este ámbito, las estrategias de Códigos Correctores de Errores (CCEs) han demostrado ser una herramienta muy potente para tratar la combinación de clasificadores binarios. No obstante, la mayoría de arquitecturas de combinación de clasificadores binarios negligen la estructura del problema multi-clase. Sin embargo, el análisis de la distribución de corrección de errores entre clases es aún un problema abierto. En esta tesis doctoral, nos centramos en tratar problemas críticos de los códigos correctores de errores; la definición del número de clasificadores necesarios para tratar un problema multi-clase arbitrario; la adaptación de los problemas binarios al problema multi-clase y cómo distribuir la corrección de errores entre clases. Para dar respuesta a estas cuestiones, en esta tesis doctoral describimos varias propuestas. 1) Definimos una nueva representación para CCEs que expresa la separabilidad entre pares de códigos y nos permite una mejor comprensión de cómo se distribuye la corrección de errores entre distintas clases. 2) Estudiamos el efecto de usar un número logarítmico de clasificadores binarios para tratar el problema multi-clase con el objetivo de obtener modelos muy eficientes. 3) Con el objetivo de encontrar modelos muy eficientes que tienen en cuenta la estructura del problema multi-clase utilizamos algoritmos genéticos que tienen en cuenta las restricciones de los ECCs. 4) Pro- ponemos un algoritmo de factorización de matrices discreta que encuentra ECCs con una configuración que distribuye corrección de error a aquellas categorías que son más propensas a tener errores. Las metodologías propuestas son evaluadas en distintos problemas reales y sintéticos como por ejemplo: Repositorio UCI de Aprendizaje Automático, reconocimiento de símbolos escritos, clasificación de señales de tráfico y reconocimiento de la pose humana. Los resultados obtenidos en esta tesis muestran mejoras significativas en rendimiento comparados con los diseños tradiciones de ECCs cuando las distintas propuestas se tienen en cuenta
Generalized Stacked Sequential Learning
[eng] Over the past few decades, machine learning (ML) algorithms have become a very useful tool in tasks where designing and programming explicit, rule-based algorithms are infeasible. Some examples of applications where machine learning has been applied successfully are spam filtering, optical character recognition (OCR), search engines and computer vision. One of the most common tasks in ML is supervised learning, where the goal is to learn a general model able to predict the correct label of unseen examples from a set of known labeled input data. In supervised learning often it is assumed that data is independent and identically distributed (i.i.d ). This means that each sample in the data set has the same probability distribution as the others and all are mutually independent. However, classification problems in real world databases can break this i.i.d. assumption. For example, consider the case of object recognition in image understanding. In this case, if one pixel belongs to a certain object category, it is very likely that neighboring pixels also belong to the same object, with the exception of the borders. Another example is the case of a laughter detection application from voice records. A laugh has a clear pattern alternating voice and non-voice segments. Thus, discriminant information comes from the alternating pattern, and not just by the samples on their own. Another example can be found in the case of signature section recognition in an e-mail. In this case, the signature is usually found at the end of the mail, thus important discriminant information is found in the context. Another case is part-of-speech tagging in which each example describes a word that is categorized as noun, verb, adjective, etc. In this case it is very unlikely that patterns such as [verb, verb, adjective, verb] occur. All these applications present a common feature: the sequence/context of the labels matters. Sequential learning (25) breaks the i.i.d. assumption and assumes that samples are not independently drawn from a joint distribution of the data samples X and their labels Y . In sequential learning the training data actually consists of sequences of pairs (x, y), so that neighboring examples exhibit some kind of correlation. Usually sequential learning applications consider one-dimensional relationship support, but these types of relationships appear very frequently in other domains, such as images, or video. Sequential learning should not be confused with time series prediction. The main difference between both problems lays in the fact that sequential learning has access to the whole data set before any prediction is made and the full set of labels is to be provided at the same time. On the other hand, time series prediction has access to real labels up to the current time t and the goal is to predict the label at t + 1. Another related but different problem is sequence classification. In this case, the problem is to predict a single label for an input sequence. If we consider the image domain, the sequential learning goal is to classify the pixels of the image taking into account their context, while sequence classification is equivalent to classify one full image as one class. Sequential learning has been addressed from different perspectives: from the point of view of meta-learning by means of sliding window techniques, recurrent sliding windows or stacked sequential learning where the method is formulated as a combination of classifiers; or from the point of view of graphical models, using for example Hidden Markov Models or Conditional Random Fields. In this thesis, we are concerned with meta-learning strategies. Cohen et al. (17) showed that stacked sequential learning (SSL from now on) performed better than CRF and HMM on a subset of problems called “sequential partitioning problems”. These problems are characterized by long runs of identical labels. Moreover, SSL is computationally very efficient since it only needs to train two classifiers a constant number of times. Considering these benefits, we decided to explore in depth sequential learning using SSL and generalize the Cohen architecture to deal with a wider variety of problems
SketChart: A Pen-Based Tool for Chart Generation and Interaction.
It has been shown that representing data with the right visualization increases the understanding of qualitative and quantitative information encoded in documents. However, current tools for generating such visualizations involve the use of traditional WIMP techniques, which perhaps makes free interaction and direct manipulation of the content harder. In this thesis, we present a pen-based prototype for data visualization using 10 different types of bar based charts. The prototype lets users sketch a chart and interact with the information once the drawing is identified. The prototype\u27s user interface consists of an area to sketch and touch based elements that will be displayed depending on the context and nature of the outline. Brainstorming and live presentations can benefit from the prototype due to the ability to visualize and manipulate data in real time. We also perform a short, informal user study to measure effectiveness of the tool while recognizing sketches and users acceptance while interacting with the system. Results show SketChart strengths and weaknesses and areas for improvement
Machine learning methods for sign language recognition: a critical review and analysis.
Sign language is an essential tool to bridge the communication gap between normal and hearing-impaired people. However, the diversity of over 7000 present-day sign languages with variability in motion position, hand shape, and position of body parts making automatic sign language recognition (ASLR) a complex system. In order to overcome such complexity, researchers are investigating better ways of developing ASLR systems to seek intelligent solutions and have demonstrated remarkable success. This paper aims to analyse the research published on intelligent systems in sign language recognition over the past two decades. A total of 649 publications related to decision support and intelligent systems on sign language recognition (SLR) are extracted from the Scopus database and analysed. The extracted publications are analysed using bibliometric VOSViewer software to (1) obtain the publications temporal and regional distributions, (2) create the cooperation networks between affiliations and authors and identify productive institutions in this context. Moreover, reviews of techniques for vision-based sign language recognition are presented. Various features extraction and classification techniques used in SLR to achieve good results are discussed. The literature review presented in this paper shows the importance of incorporating intelligent solutions into the sign language recognition systems and reveals that perfect intelligent systems for sign language recognition are still an open problem. Overall, it is expected that this study will facilitate knowledge accumulation and creation of intelligent-based SLR and provide readers, researchers, and practitioners a roadmap to guide future direction
QUIS-CAMPI: Biometric Recognition in Surveillance Scenarios
The concerns about individuals security have justified the increasing number of surveillance
cameras deployed both in private and public spaces. However, contrary to popular belief,
these devices are in most cases used solely for recording, instead of feeding intelligent analysis
processes capable of extracting information about the observed individuals. Thus, even though
video surveillance has already proved to be essential for solving multiple crimes, obtaining relevant
details about the subjects that took part in a crime depends on the manual inspection
of recordings. As such, the current goal of the research community is the development of
automated surveillance systems capable of monitoring and identifying subjects in surveillance
scenarios. Accordingly, the main goal of this thesis is to improve the performance of biometric
recognition algorithms in data acquired from surveillance scenarios. In particular, we aim at
designing a visual surveillance system capable of acquiring biometric data at a distance (e.g.,
face, iris or gait) without requiring human intervention in the process, as well as devising biometric
recognition methods robust to the degradation factors resulting from the unconstrained
acquisition process.
Regarding the first goal, the analysis of the data acquired by typical surveillance systems
shows that large acquisition distances significantly decrease the resolution of biometric samples,
and thus their discriminability is not sufficient for recognition purposes. In the literature,
diverse works point out Pan Tilt Zoom (PTZ) cameras as the most practical way for acquiring
high-resolution imagery at a distance, particularly when using a master-slave configuration. In
the master-slave configuration, the video acquired by a typical surveillance camera is analyzed
for obtaining regions of interest (e.g., car, person) and these regions are subsequently imaged
at high-resolution by the PTZ camera. Several methods have already shown that this configuration
can be used for acquiring biometric data at a distance. Nevertheless, these methods
failed at providing effective solutions to the typical challenges of this strategy, restraining its
use in surveillance scenarios. Accordingly, this thesis proposes two methods to support the development
of a biometric data acquisition system based on the cooperation of a PTZ camera
with a typical surveillance camera. The first proposal is a camera calibration method capable
of accurately mapping the coordinates of the master camera to the pan/tilt angles of the PTZ
camera. The second proposal is a camera scheduling method for determining - in real-time -
the sequence of acquisitions that maximizes the number of different targets obtained, while
minimizing the cumulative transition time. In order to achieve the first goal of this thesis,
both methods were combined with state-of-the-art approaches of the human monitoring field
to develop a fully automated surveillance capable of acquiring biometric data at a distance and
without human cooperation, designated as QUIS-CAMPI system.
The QUIS-CAMPI system is the basis for pursuing the second goal of this thesis. The analysis
of the performance of the state-of-the-art biometric recognition approaches shows that these
approaches attain almost ideal recognition rates in unconstrained data. However, this performance
is incongruous with the recognition rates observed in surveillance scenarios. Taking into
account the drawbacks of current biometric datasets, this thesis introduces a novel dataset comprising
biometric samples (face images and gait videos) acquired by the QUIS-CAMPI system at a
distance ranging from 5 to 40 meters and without human intervention in the acquisition process.
This set allows to objectively assess the performance of state-of-the-art biometric recognition
methods in data that truly encompass the covariates of surveillance scenarios. As such, this set
was exploited for promoting the first international challenge on biometric recognition in the wild. This thesis describes the evaluation protocols adopted, along with the results obtained
by the nine methods specially designed for this competition. In addition, the data acquired by
the QUIS-CAMPI system were crucial for accomplishing the second goal of this thesis, i.e., the
development of methods robust to the covariates of surveillance scenarios. The first proposal
regards a method for detecting corrupted features in biometric signatures inferred by a redundancy
analysis algorithm. The second proposal is a caricature-based face recognition approach
capable of enhancing the recognition performance by automatically generating a caricature
from a 2D photo. The experimental evaluation of these methods shows that both approaches
contribute to improve the recognition performance in unconstrained data.A crescente preocupação com a segurança dos indivíduos tem justificado o crescimento
do número de câmaras de vídeo-vigilância instaladas tanto em espaços privados como públicos.
Contudo, ao contrário do que normalmente se pensa, estes dispositivos são, na maior parte dos
casos, usados apenas para gravação, não estando ligados a nenhum tipo de software inteligente
capaz de inferir em tempo real informações sobre os indivíduos observados. Assim, apesar de a
vídeo-vigilância ter provado ser essencial na resolução de diversos crimes, o seu uso está ainda
confinado à disponibilização de vídeos que têm que ser manualmente inspecionados para extrair
informações relevantes dos sujeitos envolvidos no crime. Como tal, atualmente, o principal
desafio da comunidade científica é o desenvolvimento de sistemas automatizados capazes de
monitorizar e identificar indivíduos em ambientes de vídeo-vigilância.
Esta tese tem como principal objetivo estender a aplicabilidade dos sistemas de reconhecimento
biométrico aos ambientes de vídeo-vigilância. De forma mais especifica, pretende-se
1) conceber um sistema de vídeo-vigilância que consiga adquirir dados biométricos a longas distâncias
(e.g., imagens da cara, íris, ou vídeos do tipo de passo) sem requerer a cooperação dos
indivíduos no processo; e 2) desenvolver métodos de reconhecimento biométrico robustos aos
fatores de degradação inerentes aos dados adquiridos por este tipo de sistemas.
No que diz respeito ao primeiro objetivo, a análise aos dados adquiridos pelos sistemas típicos
de vídeo-vigilância mostra que, devido à distância de captura, os traços biométricos amostrados
não são suficientemente discriminativos para garantir taxas de reconhecimento aceitáveis.
Na literatura, vários trabalhos advogam o uso de câmaras Pan Tilt Zoom (PTZ) para adquirir
imagens de alta resolução à distância, principalmente o uso destes dispositivos no modo masterslave.
Na configuração master-slave um módulo de análise inteligente seleciona zonas de interesse
(e.g. carros, pessoas) a partir do vídeo adquirido por uma câmara de vídeo-vigilância
e a câmara PTZ é orientada para adquirir em alta resolução as regiões de interesse. Diversos
métodos já mostraram que esta configuração pode ser usada para adquirir dados biométricos
à distância, ainda assim estes não foram capazes de solucionar alguns problemas relacionados
com esta estratégia, impedindo assim o seu uso em ambientes de vídeo-vigilância. Deste modo,
esta tese propõe dois métodos para permitir a aquisição de dados biométricos em ambientes de
vídeo-vigilância usando uma câmara PTZ assistida por uma câmara típica de vídeo-vigilância. O
primeiro é um método de calibração capaz de mapear de forma exata as coordenadas da câmara
master para o ângulo da câmara PTZ (slave) sem o auxílio de outros dispositivos óticos. O
segundo método determina a ordem pela qual um conjunto de sujeitos vai ser observado pela
câmara PTZ. O método proposto consegue determinar em tempo-real a sequência de observações
que maximiza o número de diferentes sujeitos observados e simultaneamente minimiza o
tempo total de transição entre sujeitos. De modo a atingir o primeiro objetivo desta tese, os
dois métodos propostos foram combinados com os avanços alcançados na área da monitorização
de humanos para assim desenvolver o primeiro sistema de vídeo-vigilância completamente automatizado
e capaz de adquirir dados biométricos a longas distâncias sem requerer a cooperação
dos indivíduos no processo, designado por sistema QUIS-CAMPI.
O sistema QUIS-CAMPI representa o ponto de partida para iniciar a investigação relacionada
com o segundo objetivo desta tese. A análise do desempenho dos métodos de reconhecimento
biométrico do estado-da-arte mostra que estes conseguem obter taxas de reconhecimento
quase perfeitas em dados adquiridos sem restrições (e.g., taxas de reconhecimento
maiores do que 99% no conjunto de dados LFW). Contudo, este desempenho não é corroborado pelos resultados observados em ambientes de vídeo-vigilância, o que sugere que os conjuntos
de dados atuais não contêm verdadeiramente os fatores de degradação típicos dos ambientes de
vídeo-vigilância. Tendo em conta as vulnerabilidades dos conjuntos de dados biométricos atuais,
esta tese introduz um novo conjunto de dados biométricos (imagens da face e vídeos do tipo de
passo) adquiridos pelo sistema QUIS-CAMPI a uma distância máxima de 40m e sem a cooperação
dos sujeitos no processo de aquisição. Este conjunto permite avaliar de forma objetiva o desempenho
dos métodos do estado-da-arte no reconhecimento de indivíduos em imagens/vídeos
capturados num ambiente real de vídeo-vigilância. Como tal, este conjunto foi utilizado para
promover a primeira competição de reconhecimento biométrico em ambientes não controlados.
Esta tese descreve os protocolos de avaliação usados, assim como os resultados obtidos por 9
métodos especialmente desenhados para esta competição. Para além disso, os dados adquiridos
pelo sistema QUIS-CAMPI foram essenciais para o desenvolvimento de dois métodos para
aumentar a robustez aos fatores de degradação observados em ambientes de vídeo-vigilância. O
primeiro é um método para detetar características corruptas em assinaturas biométricas através
da análise da redundância entre subconjuntos de características. O segundo é um método de
reconhecimento facial baseado em caricaturas automaticamente geradas a partir de uma única
foto do sujeito. As experiências realizadas mostram que ambos os métodos conseguem reduzir
as taxas de erro em dados adquiridos de forma não controlada
- …