19 research outputs found
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 indivĂduos tem justificado o crescimento
do nĂşmero de câmaras de vĂdeo-vigilância instaladas tanto em espaços privados como pĂşblicos.
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 indivĂduos observados. Assim, apesar de a
vĂdeo-vigilância ter provado ser essencial na resolução de diversos crimes, o seu uso está ainda
confinado Ă disponibilização de vĂdeos que tĂŞm que ser manualmente inspecionados para extrair
informações relevantes dos sujeitos envolvidos no crime. Como tal, atualmente, o principal
desafio da comunidade cientĂfica Ă© o desenvolvimento de sistemas automatizados capazes de
monitorizar e identificar indivĂduos em ambientes de vĂdeo-vigilância.
Esta tese tem como principal objetivo estender a aplicabilidade dos sistemas de reconhecimento
biomĂ©trico aos ambientes de vĂdeo-vigilância. De forma mais especifica, pretende-se
1) conceber um sistema de vĂdeo-vigilância que consiga adquirir dados biomĂ©tricos a longas distâncias
(e.g., imagens da cara, Ăris, ou vĂdeos do tipo de passo) sem requerer a cooperação dos
indivĂduos no processo; e 2) desenvolver mĂ©todos de reconhecimento biomĂ©trico 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 tĂpicos
de vĂdeo-vigilância mostra que, devido Ă distância de captura, os traços biomĂ©tricos 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 vĂdeo adquirido por uma câmara de vĂdeo-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 estratĂ©gia, impedindo assim o seu uso em ambientes de vĂdeo-vigilância. Deste modo,
esta tese propõe dois métodos para permitir a aquisição de dados biométricos em ambientes de
vĂdeo-vigilância usando uma câmara PTZ assistida por uma câmara tĂpica de vĂdeo-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 auxĂlio de outros dispositivos Ăłticos. 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 nĂşmero 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 vĂdeo-vigilância completamente automatizado
e capaz de adquirir dados biométricos a longas distâncias sem requerer a cooperação
dos indivĂduos 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 nĂŁo Ă© corroborado pelos resultados observados em ambientes de vĂdeo-vigilância, o que sugere que os conjuntos
de dados atuais nĂŁo contĂŞm verdadeiramente os fatores de degradação tĂpicos dos ambientes de
vĂdeo-vigilância. Tendo em conta as vulnerabilidades dos conjuntos de dados biomĂ©tricos atuais,
esta tese introduz um novo conjunto de dados biomĂ©tricos (imagens da face e vĂdeos 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 mĂ©todos do estado-da-arte no reconhecimento de indivĂduos em imagens/vĂdeos
capturados num ambiente real de vĂdeo-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 vĂdeo-vigilância. O
primeiro Ă© um mĂ©todo para detetar caracterĂsticas corruptas em assinaturas biomĂ©tricas atravĂ©s
da análise da redundância entre subconjuntos de caracterĂsticas. O segundo Ă© um mĂ©todo de
reconhecimento facial baseado em caricaturas automaticamente geradas a partir de uma Ăşnica
foto do sujeito. As experiências realizadas mostram que ambos os métodos conseguem reduzir
as taxas de erro em dados adquiridos de forma nĂŁo controlada
A Deep Learning Approach for Spatiotemporal-Data-Driven Traffic State Estimation
The past decade witnessed rapid developments in traffic data sensing technologies in the form of roadside detector hardware, vehicle on-board units, and pedestrian wearable devices. The growing magnitude and complexity of the available traffic data has fueled the demand for data-driven models that can handle large scale inputs. In the recent past, deep-learning-powered algorithms have become the state-of-the-art for various data-driven applications. In this research, three applications of deep learning algorithms for traffic state estimation were investigated. Firstly, network-wide traffic parameters estimation was explored. An attention-based multi-encoder-decoder (Att-MED) neural network architecture was proposed and trained to predict freeway traffic speed up to 60 minutes ahead. Att-MED was designed to encode multiple traffic input sequences: short-term, daily, and weekly cyclic behavior. The proposed network produced an average prediction accuracy of 97.5%, which was superior to the compared baseline models. In addition to improving the output performance, the model\u27s attention weights enhanced the model interpretability. This research additionally explored the utility of low-penetration connected probe-vehicle data for network-wide traffic parameters estimation and prediction on freeways. A novel sequence-to-sequence recurrent graph networks (Seq2Se2 GCN-LSTM) was designed. It was then trained to estimate and predict traffic volume and speed for a 60-minute future time horizon. The proposed methodology generated volume and speed predictions with an average accuracy of 90.5% and 96.6%, respectively, outperforming the investigated baseline models. The proposed method demonstrated robustness against perturbations caused by the probe vehicle fleet\u27s low penetration rate. Secondly, the application of deep learning for road weather detection using roadside CCTVs were investigated. A Vision Transformer (ViT) was trained for simultaneous rain and road surface condition classification. Next, a Spatial Self-Attention (SSA) network was designed to consume the individual detection results, interpret the spatial context, and modify the collective detection output accordingly. The sequential module improved the accuracy of the stand-alone Vision Transformer as measured by the F1-score, raising the total accuracy for both tasks to 96.71% and 98.07%, respectively. Thirdly, a real-time video-based traffic incident detection algorithm was developed to enhance the utilization of the existing roadside CCTV network. The methodology automatically identified the main road regions in video scenes and investigated static vehicles around those areas. The developed algorithm was evaluated using a dataset of roadside videos. The incidents were detected with 85.71% sensitivity and 11.10% false alarm rate with an average delay of 27.53 seconds. In general, the research proposed in this dissertation maximizes the utility of pre-existing traffic infrastructure and emerging probe traffic data. It additionally demonstrated deep learning algorithms\u27 capability of modeling complex spatiotemporal traffic data. This research illustrates that advances in the deep learning field continue to have a high applicability potential in the traffic state estimation domain
Image-set, Temporal and Spatiotemporal Representations of Videos for Recognizing, Localizing and Quantifying Actions
This dissertation addresses the problem of learning video representations, which is defined here as transforming the video so that its essential structure is made more visible or accessible for action recognition and quantification. In the literature, a video can be represented by a set of images, by modeling motion or temporal dynamics, and by a 3D graph with pixels as nodes. This dissertation contributes in proposing a set of models to localize, track, segment, recognize and assess actions such as (1) image-set models via aggregating subset features given by regularizing normalized CNNs, (2) image-set models via inter-frame principal recovery and sparsely coding residual actions, (3) temporally local models with spatially global motion estimated by robust feature matching and local motion estimated by action detection with motion model added, (4) spatiotemporal models 3D graph and 3D CNN to model time as a space dimension, (5) supervised hashing by jointly learning embedding and quantization, respectively. State-of-the-art performances are achieved for tasks such as quantifying facial pain and human diving. Primary conclusions of this dissertation are categorized as follows: (i) Image set can capture facial actions that are about collective representation; (ii) Sparse and low-rank representations can have the expression, identity and pose cues untangled and can be learned via an image-set model and also a linear model; (iii) Norm is related with recognizability; similarity metrics and loss functions matter; (v) Combining the MIL based boosting tracker with the Particle Filter motion model induces a good trade-off between the appearance similarity and motion consistence; (iv) Segmenting object locally makes it amenable to assign shape priors; it is feasible to learn knowledge such as shape priors online from Web data with weak supervision; (v) It works locally in both space and time to represent videos as 3D graphs; 3D CNNs work effectively when inputted with temporally meaningful clips; (vi) the rich labeled images or videos help to learn better hash functions after learning binary embedded codes than the random projections. In addition, models proposed for videos can be adapted to other sequential images such as volumetric medical images which are not included in this dissertation
A Non-Intrusive Multi-Sensor RGB-D System for Preschool Classroom Behavior Analysis
University of Minnesota Ph.D. dissertation. May 2017. Major: Computer Science. Advisor: Nikolaos Papanikolopoulos. 1 computer file (PDF); vii, 121 pages + 2 mp4 video filesMental health disorders are a leading cause of disability in North America and can represent a significant source of financial burden. Early intervention is a key aspect in treating mental disorders as it can dramatically increase the probability of a positive outcome. One key factor to early intervention is the knowledge of risk-markers -- genetic, neural, behavioral and/or social deviations -- that indicate the development of a particular mental disorder. Once these risk-markers are known, it is important to have tools for reliable identification of these risk-markers. For visually observable risk-markers, discovery and screening ideally should occur in a natural environment. However, this often incurs a high cost. Current advances in technology allow for the development of assistive systems that could aid in the detection and screening of visually observable risk-markers in every-day environments, like a preschool classroom. This dissertation covers the development of such a system. The system consists of a series of networked sensors that are able to collect data from a wide baseline. These sensors generate color images and depth maps that can be used to create a 3D point cloud reconstruction of the classroom. The wide baseline nature of the setup helps to minimize the effects of occlusion, since data is captured from multiple distinct perspectives. These point clouds are used to detect occupants in the room and track them throughout their activities. This tracking information is then used to analyze classroom and individual behaviors, enabling the screening for specific risk-markers and also the ability to create a corpus of data that could be used to discover new risk-markers. This system has been installed at the Shirley G. Moore Lab school, a research preschool classroom in the Institute of Child Development at the University of Minnesota. Recordings have been taken and analyzed from actual classes. No instruction or pre-conditioning was given to the instructors or the children in these classes. Portions of this data have also been manually annotated to create groundtruth data that was used to validate the efficacy of the proposed system
Cyber-Physical Threat Intelligence for Critical Infrastructures Security
Modern critical infrastructures can be considered as large scale Cyber Physical Systems (CPS). Therefore, when designing, implementing, and operating systems for Critical Infrastructure Protection (CIP), the boundaries between physical security and cybersecurity are blurred. Emerging systems for Critical Infrastructures Security and Protection must therefore consider integrated approaches that emphasize the interplay between cybersecurity and physical security techniques. Hence, there is a need for a new type of integrated security intelligence i.e., Cyber-Physical Threat Intelligence (CPTI). This book presents novel solutions for integrated Cyber-Physical Threat Intelligence for infrastructures in various sectors, such as Industrial Sites and Plants, Air Transport, Gas, Healthcare, and Finance. The solutions rely on novel methods and technologies, such as integrated modelling for cyber-physical systems, novel reliance indicators, and data driven approaches including BigData analytics and Artificial Intelligence (AI). Some of the presented approaches are sector agnostic i.e., applicable to different sectors with a fair customization effort. Nevertheless, the book presents also peculiar challenges of specific sectors and how they can be addressed. The presented solutions consider the European policy context for Security, Cyber security, and Critical Infrastructure protection, as laid out by the European Commission (EC) to support its Member States to protect and ensure the resilience of their critical infrastructures. Most of the co-authors and contributors are from European Research and Technology Organizations, as well as from European Critical Infrastructure Operators. Hence, the presented solutions respect the European approach to CIP, as reflected in the pillars of the European policy framework. The latter includes for example the Directive on security of network and information systems (NIS Directive), the Directive on protecting European Critical Infrastructures, the General Data Protection Regulation (GDPR), and the Cybersecurity Act Regulation. The sector specific solutions that are described in the book have been developed and validated in the scope of several European Commission (EC) co-funded projects on Critical Infrastructure Protection (CIP), which focus on the listed sectors. Overall, the book illustrates a rich set of systems, technologies, and applications that critical infrastructure operators could consult to shape their future strategies. It also provides a catalogue of CPTI case studies in different sectors, which could be useful for security consultants and practitioners as well
Cyber-Physical Threat Intelligence for Critical Infrastructures Security
Modern critical infrastructures can be considered as large scale Cyber Physical Systems (CPS). Therefore, when designing, implementing, and operating systems for Critical Infrastructure Protection (CIP), the boundaries between physical security and cybersecurity are blurred. Emerging systems for Critical Infrastructures Security and Protection must therefore consider integrated approaches that emphasize the interplay between cybersecurity and physical security techniques. Hence, there is a need for a new type of integrated security intelligence i.e., Cyber-Physical Threat Intelligence (CPTI). This book presents novel solutions for integrated Cyber-Physical Threat Intelligence for infrastructures in various sectors, such as Industrial Sites and Plants, Air Transport, Gas, Healthcare, and Finance. The solutions rely on novel methods and technologies, such as integrated modelling for cyber-physical systems, novel reliance indicators, and data driven approaches including BigData analytics and Artificial Intelligence (AI). Some of the presented approaches are sector agnostic i.e., applicable to different sectors with a fair customization effort. Nevertheless, the book presents also peculiar challenges of specific sectors and how they can be addressed. The presented solutions consider the European policy context for Security, Cyber security, and Critical Infrastructure protection, as laid out by the European Commission (EC) to support its Member States to protect and ensure the resilience of their critical infrastructures. Most of the co-authors and contributors are from European Research and Technology Organizations, as well as from European Critical Infrastructure Operators. Hence, the presented solutions respect the European approach to CIP, as reflected in the pillars of the European policy framework. The latter includes for example the Directive on security of network and information systems (NIS Directive), the Directive on protecting European Critical Infrastructures, the General Data Protection Regulation (GDPR), and the Cybersecurity Act Regulation. The sector specific solutions that are described in the book have been developed and validated in the scope of several European Commission (EC) co-funded projects on Critical Infrastructure Protection (CIP), which focus on the listed sectors. Overall, the book illustrates a rich set of systems, technologies, and applications that critical infrastructure operators could consult to shape their future strategies. It also provides a catalogue of CPTI case studies in different sectors, which could be useful for security consultants and practitioners as well
Advanced extravehicular activity systems requirements definition study. Phase 2: Extravehicular activity at a lunar base
The focus is on Extravehicular Activity (EVA) systems requirements definition for an advanced space mission: remote-from-main base EVA on the Moon. The lunar environment, biomedical considerations, appropriate hardware design criteria, hardware and interface requirements, and key technical issues for advanced lunar EVA were examined. Six remote EVA scenarios (three nominal operations and three contingency situations) were developed in considerable detail