En la actualidad, la cámara de vídeo se ha convertido en un dispositivo omnipresente. Debido a su miniaturización, estas se pueden encontrar integradas en multitud de dispositivos de uso diario, desde teléfonos móviles o tabletas, hasta ordenadores portátiles. Aunque estos dispositivos son empleados por millones de personas diariamente de forma inofensiva, capturando vídeo, realizando fotografías que luego son compartidas, etc.; el empleo de videocámaras para tareas de videovigilancia levanta cierta preocupación entre la población, sobre todo cuando estas forman parte de sistemas inteligentes de monitorización. Esto supone una amenaza para la privacidad debido a que las grabaciones realizadas por estos sistemas contienen una gran cantidad de información que puede ser extraída de forma automática mediante técnicas de visión artificial. Sin embargo, la aplicación de esta tecnología en diversas áreas puede suponer un impacto muy positivo para las personas. Por otro lado, la población mundial está envejeciendo rápidamente. Este cambio demográfico provocará que un mayor número de personas en situación de dependencia, o que requieren apoyo en su vida diaria, vivan solas. Por lo que se hace necesario encontrar una solución que permita extender su autonomía. La vida asistida por el entorno (AAL por sus siglas en inglés) ofrece una solución aportando inteligencia al entorno donde residen la personas de modo que este les asista en sus actividades diarias. Estos entornos requieren la instalación de sensores para la captura de datos. La utilización de videocámaras, con la riqueza en los datos que ofrecen, en entornos privados haría posible la creación de servicios AAL orientados hacia el cuidado de las personas como, por ejemplo, la detección de accidentes en el hogar, detección temprana de problemas cognitivos y muchos otros. Sin embargo, dada la sencilla interpretación de imágenes por las personas, esto plantea problemas éticos que afectan a la privacidad. En este trabajo se propone una solución para poder hacer uso de videocámaras en entornos privados con el objetivo de dar soporte a las personas y habilitar así el desarrollo de servicios de la vida asistida por el entorno en un hogar inteligente. En concreto, se propone la protección de la privacidad en aquellos servicios AAL de monitorización que requieren acceso al vídeo por parte de un cuidador, ya sea profesional o informal. Esto sucede, por ejemplo, cuando se detecta un accidente en un sistema de monitorización y ese evento requiere la confirmación visual de lo ocurrido. Igualmente, en servicios AAL de telerehabilitación puede ser requerida la supervisión por parte de un humano. En este tipo de escenarios es fundamental proteger la privacidad en el momento en que se esté accediendo u observando el vídeo. Como parte de este trabajo se ha llevado a cabo el estudio del estado de la cuestión en la cual se han revisado los métodos de protección de la privacidad visual presentes en la literatura. Esta revisión es la primera en realizar un análisis exhaustivo de este tema centrándose, principalmente, en los métodos de protección. Como resultado, se ha desarrollado un esquema de protección de la privacidad visual basado en el reconocimiento del contexto que permite adecuar el nivel de privacidad durante la observación cuando las preferencias del usuario coinciden con el contexto. La detección del contexto es necesaria para poder detectar en la escena las circunstancias en que el usuario demanda determinado nivel de privacidad. Mediante la utilización de este esquema, cada uno de los fotogramas que componen un flujo de vídeo en directo es modificado antes de su transmisión teniendo en cuenta los requisitos de privacidad del usuario. El esquema propuesto hace uso de diversas técnicas de modificación de imágenes para proteger la privacidad, así como de visión artificial para reconocer dicho contexto. Por tanto, en esta tesis doctoral se realizan diversas contribuciones en distintas áreas con el objetivo de llevar a cabo el desarrollo del esquema propuesto de protección de la privacidad visual. De este modo, se espera que los resultados obtenidos nos sitúen un paso más cerca de la utilización de videocámaras en entornos privados, incrementando su aceptación y haciendo posible la implantación de servicios AAL basados en visión artificial que permitan aumentar la autonomía de las personas en situación de dependencia.

Background: Video surveillance is a growing area where it can help with deterring crime, support investigation or to help gather statistics. These are just some areas where video surveillance can aid society. However, there is an improvement that could increase the efficiency of video surveillance by introducing tracking. More specifically, tracking between cameras in a network. Automating this process could reduce the need for humans to monitor and review since the tracking can track and inform the relevant people on its own. This has a wide array of usability areas, such as forensic investigation, crime alerting, or tracking down people who have disappeared. Objectives: What we want to investigate is the common setup of real-time multi-target multi-camera tracking (MTMCT) systems. Next up, we want to investigate how the components in an MTMCT system affect each other and the complete system. Lastly, we want to see how image enhancement can affect the MTMCT. Methods: To achieve our objectives, we have conducted a systematic literature review to gather information. Using the information, we implemented an MTMCT system where we evaluated the components to see how they interact in the complete system. Lastly, we implemented two image enhancement techniques to see how they affect the MTMCT. Results: As we have discovered, most often, MTMCT is constructed using a detection for discovering object, tracking to keep track of the objects in a single camera and a re-identification method to ensure that objects across cameras have the same ID. The different components have quite a considerable effect on each other where they can sabotage and improve each other. An example could be that the quality of the bounding boxes affect the data which re-identification can extract. We discovered that the image enhancement we used did not introduce any significant improvement. Conclusions: The most common structure for MTMCT are detection, tracking and re-identification. From our finding, we can see that all the component affect each other, but re-identification is the one that is mostly affected by the other components and the image enhancement. The two tested image enhancement techniques could not introduce enough improvement, but other image enhancement could be used to make the MTMCT perform better. The MTMCT system we constructed did not manage to reach real-time.

A identificação de modelos é usualmente a tarefa mais significativa e demorada no trabalho de implementação e manutenção de sistemas de controle que usam Controle Preditivo baseado em Modelos (MPC) tendo em vista a complexidade da tarefa e a importância que o modelo possui para um bom desempenho do controlador. Após a implementação, o controlador tende a permanecer com o modelo original mesmo que mudanças de processo tenham ocorrido levando a uma degradação das ações do controlador. Este trabalho apresenta uma aplicação industrial de re-identificação em malha fechada. A metodologia de excitação da planta utilizada foi apresentada em Sotomayor et al. (2009). Tal técnica permite obter o comportamento das variáveis de processo sem desligar o MPC e sem modificar sua estrutura, aumentando assim, o automatismo e a segurança do procedimento de re-identificação. O sistema re-identificado foi uma coluna debutanizadora de uma refinaria brasileira sendo que os modelos fazem parte do controle preditivo multivariável dessa coluna de destilação. A metodologia foi aplicada com sucesso podendo-se obter os seis novos modelos para atualizar o controlador em questão, o que resultou em uma melhoria de seu desempenho. / Model identification is usually the most significant and time-consuming task of implementing and maintaining control systems based on models (MPC) concerning the complexity of the task and the importance of the model for a good performance of the controller. After being implemented the MPC tends to remain with the original model even after process changes have occurred, leading to a degradation of the controller actions. The present work shows an industrial application of closed-loop re-identification. The plant excitation methodology used here was presented in Sotomayor et al. (2009). Such technique allows for obtaining the behavior of the process variables with the MPC still working and without modifying the MPC structure, increasing automation and safety of the re-identification procedure. The system re-identified was a debutanizer column of a Brazilian refinery being the models part of the multivariable predictive control of this distillation column. The methodology was applied with reasonable success managing to obtain 6 new models to update this MPC, and resulting in improved control performance.

Submitted by Almir Azevedo (barbio1313@gmail.com) on 2015-08-24T17:42:07Z No. of bitstreams: 1 CARLOS ALEXANDRE F. JORGE D.pdf: 11356748 bytes, checksum: 59927b7a303fb41d249f403942824b9a (MD5) / Made available in DSpace on 2015-08-24T17:42:07Z (GMT). No. of bitstreams: 1 CARLOS ALEXANDRE F. JORGE D.pdf: 11356748 bytes, checksum: 59927b7a303fb41d249f403942824b9a (MD5) Previous issue date: 2015-07 / This work proposes the development of a system to evaluate received radiation dose for nuclear plants personnel. The system is conceived to operate in a complementary form to the existing approaches for radiological protection, thus o ering redundancy, what is desirable for critical plants operation. The proposed system must operate in an independent form on the actions to be performed by the operators under evaluation. Therefore, it was decided it would be based on methods used for video surveillance. The nuclear plant used as example is Argonauta Nuclear Research Reactor, belonging to Instituto de Engenharia Nuclear, Comiss~ao Nacional de Energia Nuclear (Nuclear Engineering Institute, National Nuclear Energy Commission). During this thesis research, both radiation dose rate distribution and video databases were obtained. Methods available in the literature, for targets detection and/or tracking, were evaluated for this database. From these results, a new system was proposed, with the purpose of meeting the requisites for this particular application. Given the tracked positions of each worker, the radiation dose received by each one during tasks execution is estimated, and may serve as part of a decision support system.

Because of the increase in the number of security cameras, there is more video footage available than a human could efficiently process. In combination with the fact that computers are getting more efficient, it is getting more and more interesting to solve the problem of detecting and recognizing people automatically. Therefore a method is proposed for estimating a 3D-path of a person of interest in multiple, non overlapping, monocular cameras. This project is a collaboration between two master theses. This thesis will focus on recognizing a person of interest from several possible candidates, as well as estimating the 3D-position of a person and providing a graphical user interface for the system. The recognition of the person of interest includes keeping track of said person frame by frame, and identifying said person in video sequences where the person of interest has not been seen before. The final product is able to both detect and recognize people in video, as well as estimating their 3D-position relative to the camera. The product is modular and any part can be improved or changed completely, without changing the rest of the product. This results in a highly versatile product which can be tailored for any given situation.

This thesis is concerned with the topic of person re-identification. Many tracking algorithms today cannot keep track of players reentering the scene from different angles and times. Therefore, in this thesis, current literature is explored to gather information about the topic, and a current state-of-the-art model is tested. The person re-identification techniques will be applied to Padel games due to the collaboration with PadelPlay AB. The purpose of the thesis is to keep track of players during full matches of Padel with correct identities. To this, a current state-of-the-art model is applied to an existing tracking algorithm to enhance its capabilities.  Furthermore, the purpose is broken down into two research questions. Firstly, how well does an existing person re-id model perform on Padel matches when it comes to keeping a consistent and accurate id on all players. Secondly, how can this model be improved upon to perform better in the new domain, being the sport of Padel? To be able to answer the research questions, a Padel dataset is created for benchmarking purposes. The state-of-the-art model is tested on the new dataset to see how it handles a new domain. Additionally, the same state-of-the-art model is retrained on the Padel dataset to answer the second research question.  The results show that the state-of-the-art model that is previously trained on the Market-1501 dataset is highly generalizable on the Padel dataset and performs closely to the new model that is purely trained on the Padel dataset. Although they perform alike, the new model trained on the Padel dataset is slightly better as seen through both the quantitative and qualitative evaluations. Furthermore, the application of re-identification technology to keep track of players yielded significantly higher results than conventional solutions such as YOLOv5 with Deepsort.

This project aims at using deep learning to solve the pedestrian tracking problem for Autonomous driving usage. The research area is in the domain of computer vision and deep learning. Multi-Object Tracking (MOT) aims at tracking multiple targets simultaneously in a video data. The main application scenarios of MOT are security monitoring and autonomous driving. In these scenarios, we often need to track many targets at the same time which is not possible with only object detection or single object tracking algorithms for their lack of stability and usability. Therefore we need to explore the area of multiple object tracking. The proposed method breaks the MOT into different stages and utilizes the motion and appearance information of targets to track them in the video data. We used three different object detectors to detect the pedestrians in frames, a person re-identification model as appearance feature extractor and Kalman filter as motion predictor. Our proposed model achieves 47.6% MOT accuracy and 53.2% in IDF1 score while the results obtained by the model without person re-identification module is only 44.8% and 45.8% respectively. Our experiment results indicate the fact that a robust multiple object tracking algorithm can be achieved by splitted tasks and improved by the representative DNN based appearance features. / Detta projekt syftar till att använda djupinlärning för att lösa problemet med att följa fotgängare för autonom körning. For ligger inom datorseende och djupinlärning. Multi-Objekt-följning (MOT) syftar till att följa flera mål samtidigt i videodata. de viktigaste applikationsscenarierna för MOT är säkerhetsövervakning och autonom körning. I dessa scenarier behöver vi ofta följa många mål samtidigt, vilket inte är möjligt med endast objektdetektering eller algoritmer för enkel följning av objekt för deras bristande stabilitet och användbarhet, därför måste utforska området för multipel objektspårning. Vår metod bryter MOT i olika steg och använder rörelse- och utseendinformation för mål för att spåra dem i videodata, vi använde tre olika objektdetektorer för att upptäcka fotgängare i ramar en personidentifieringsmodell som utseendefunktionsavskiljare och Kalmanfilter som rörelsesprediktor. Vår föreslagna modell uppnår 47,6 % MOT-noggrannhet och 53,2 % i IDF1 medan resultaten som erhållits av modellen utan personåteridentifieringsmodul är endast 44,8%respektive 45,8 %. Våra experimentresultat visade att den robusta algoritmen för multipel objektspårning kan uppnås genom delade uppgifter och förbättras av de representativa DNN-baserade utseendefunktionerna.

Η παρούσα διπλωματική εργασία μελετά την ανίχνευση και παρακολούθηση της κίνησης των ανθρώπων μέσα από δίκτυα καμερών. Σκοπός της παρούσας εργασίας είναι η υλοποίηση ενός συστήματος ανίχνευσης , παρακολούθησης εκ νέου ταυτοποίησης των ανθρώπων που διέρχονται μέσα από ένα δίκτυο καμερών καθώς και να προτείνει ένα μοντέλο για την κατανόηση της τοπολογίας του δικτύου των καμερών. Το κύριο πρόβλημα υποδιαιρείται σε τρία επιμέρους υπό – προβλήματα. Το πρώτο αφορά την ανίχνευση κίνησης. Το δεύτερο την παρακολούθηση των ανθρώπων και τέλος το τρίτο αφορά την αντιστοίχηση τους μεταξύ των καμερών. Σαν αποτέλεσμα στο τέλος έχουμε για κάθε άνθρωπο το μονοπάτι που διέγραψε μέσα στο δίκτυο. Η Ανίχνευση κίνησης υλοποιείται με αφαίρεση φόντου. Η παρακολούθηση υλοποιείται με δύο χαρακτηριστικά, αυτά του κέντρου μάζας και του χρωματικού ιστογράμματος. Η τοπολογία του δικτύου ανακαλύπτεται με ένα μοντέλο που καταγράφει σημεία εισόδου και εξόδου συσχετισμένα με την αντίστοιχη κάμερα από την οποία εισήλθαν ή στην οποία εξήλθαν αντίστοιχα οι άνθρωποι. Κατόπιν γίνεται αντιστοίχηση των σημείων αυτών στις κρίσιμες περιοχές της κάθε κάμερας και η πλειοψηφία των συσχετίσεων τους ορίζει την επικοινωνούσα , για αυτές τις περιοχές , κάμερα. Τέλος γίνεται η αντιστοίχηση των διαδρομών μεταξύ καμερών με έλεγχο χώρο-χρονικών χαρακτηριστικών και χαρακτηριστικών εμφάνισης. Το σύστημα υλοποιήθηκε σε Matlab και έτρεξε σε Intel i7 με συχνότητα 2.93 Ghz και 8GB μνήμης ram. Οι αλγόριθμοι λειτούργησαν ικανοποιητικά με πολύ καλά αποτελέσματα, και μπορούν να περάσουν ως είσοδοι σε πληθώρα εφαρμογών υψηλοτέρου επιπέδου που έχουν ως σκοπό την αναγνώριση της ανθρώπινης δραστηριότητας και την κατανόηση συμπεριφοράς. / This thesis deals with the detection and motion tracking through camera networks. Its purpose is to implement a system for monitoring human movement and perform re-identification in camera networks. It also proposes a model for discovering the topology of cameras network. The main problem is divided into three sub – problems. The first one deals with motion detection , the second one tracks every human located in the plane, and finally the third one has to do with the re-identification between the cameras. As a result we find and identify all human’s paths traced in the network. At first we start with detection that involves also background subtraction. The background is recovered in a dynamic way at every frame and involves median selection. Tracking is accomplished using two features, the centroid and the color histogram. Network topology is discovered from a model which reports entry and exit points associated with the corresponding camera. The system is implemented in Matlab and runs on Intel i7 with frequency 2.93 Ghz and 8GB of ram. The algorithms perform well producing very good results, and can be fed as inputs to a variety of applications that deal with problems related to higher level recognition of human activity and behavior understanding.

There has been a significant number of security concerns in recent times; as a result, security cameras have been installed to monitor activities and to prevent crimes in most public places. These analysis are done either through video analytic or forensic analysis operations on human observations. To this end, within the research context of this thesis, a proactive machine vision based military recognition system has been developed to help monitor activities in the military environment. The proposed object detection, recognition and re-identification systems have been presented in this thesis. A novel technique for military personnel recognition is presented in this thesis. Initially the detected camouflaged personnel are segmented using a grabcut segmentation algorithm. Since in general a camouflaged personnel's uniform appears to be similar both at the top and the bottom of the body, an image patch is initially extracted from the segmented foreground image and used as the region of interest. Subsequently the colour and texture features are extracted from each patch and used for classification. A second approach for personnel recognition is proposed through the recognition of the badge on the cap of a military person. A feature matching metric based on the extracted Speed Up Robust Features (SURF) from the badge on a personnel's cap enabled the recognition of the personnel's arm of service. A state-of-the-art technique for recognising vehicle types irrespective of their view angle is also presented in this thesis. Vehicles are initially detected and segmented using a Gaussian Mixture Model (GMM) based foreground/background segmentation algorithm. A Canny Edge Detection (CED) stage, followed by morphological operations are used as pre-processing stage to help enhance foreground vehicular object detection and segmentation. Subsequently, Region, Histogram Oriented Gradient (HOG) and Local Binary Pattern (LBP) features are extracted from the refined foreground vehicle object and used as features for vehicle type recognition. Two different datasets with variant views of front/rear and angle are used and combined for testing the proposed technique. For night-time video analytics and forensics, the thesis presents a novel approach to pedestrian detection and vehicle type recognition. A novel feature acquisition technique named, CENTROG, is proposed for pedestrian detection and vehicle type recognition in this thesis. Thermal images containing pedestrians and vehicular objects are used to analyse the performance of the proposed algorithms. The video is initially segmented using a GMM based foreground object segmentation algorithm. A CED based pre-processing step is used to enhance segmentation accuracy prior using Census Transforms for initial feature extraction. HOG features are then extracted from the Census transformed images and used for detection and recognition respectively of human and vehicular objects in thermal images. Finally, a novel technique for people re-identification is proposed in this thesis based on using low-level colour features and mid-level attributes. The low-level colour histogram bin values were normalised to 0 and 1. A publicly available dataset (VIPeR) and a self constructed dataset have been used in the experiments conducted with 7 clothing attributes and low-level colour histogram features. These 7 attributes are detected using features extracted from 5 different regions of a detected human object using an SVM classifier. The low-level colour features were extracted from the regions of a detected human object. These 5 regions are obtained by human object segmentation and subsequent body part sub-division. People are re-identified by computing the Euclidean distance between a probe and the gallery image sets. The experiments conducted using SVM classifier and Euclidean distance has proven that the proposed techniques attained all of the aforementioned goals. The colour and texture features proposed for camouflage military personnel recognition surpasses the state-of-the-art methods. Similarly, experiments prove that combining features performed best when recognising vehicles in different views subsequent to initial training based on multi-views. In the same vein, the proposed CENTROG technique performed better than the state-of-the-art CENTRIST technique for both pedestrian detection and vehicle type recognition at night-time using thermal images. Finally, we show that the proposed 7 mid-level attributes and the low-level features results in improved performance accuracy for people re-identification.

L'essor des systèmes mobiles pose de nouvelles problématiques dans le domaine de vision par ordinateur. Les techniques de ré-identification s'appuyant sur un réseau de caméras fixes doivent être repensées afin de s'adapter à un décor changeant. Pour répondre à ces besoins, cette thèse explore, dans le cadre du corps humain, l'utilisation d'un modèle structurel habituellement employé pour de la reconnaissance faciale. Il s'agit de l'alignement d'un modèle à distribution de points (Point Distribution Model ou PDM). L'objectif de ce pré-traitement avant la ré-identification est triple, segmenter la personne du décor, améliorer la robustesse vis-à-vis de sa pose et extraire des points clés spatiaux pour construire une signature basée sur son comportement.Nous concevons et évaluons un système complet de ré-identification, découpé en trois modules mis en séquence. Le premier de ces modules correspond à la détection de personnes. Nous proposons de nous baser sur une méthode de l'état de l'art utilisant les Channel Features avec l'algorithme AdaBoost.Le second module est l'alignement du PDM au sein de la boîte englobante fournie par la détection. Deux approches sont présentées dans cette thèse. La première s'appuie sur une formulation paramétrique du modèle de forme. L'alignement de ce modèle est guidé par la maximisation d'un score d'un modèle d'apparence GentleBoost utilisant des caractéristiques locales de type histogrammes de gradients orientés. La seconde approche exploite une technique de cascade de régressions de forme. L'idée principale est le regroupement de déformations homogènes en clusters et la classification de ces derniers dans le but d'aligner le PDM itérativement.Enfin, le troisième module est celui de la ré-identification. Nous montrons que l'utilisation d'un PDM en support permet d'améliorer les résultats de ré-identification. Nos expérimentations portent sur des signatures d'apparence classique, les histogrammes de couleurs, et sur un descripteur de forme, le Shape Context. L'évaluation de ce dernier fournit des résultats encourageants pour une perspective d'utilisation des PDM au sein d'une reconnaissance de démarches. / The emergence of mobile systems brings new problematics in computer vision. Static camera-based methods for re-identification need to be adapted in this new context. To deal with dynamical background, this thesis proposes to employ the well known Point Distribution Model (PDM), usually applied for face alignment, on the human body. Three advantages come from this pre-processing before re-identification, segment the person from background, enhance robustness to the person pose and extract spatial key points to build a behavioural-based signature.We implement and evaluate a complete framework for re-identification, divided in three sequential modules. The first one corresponds to the pedestrian detection. We use an efficient method of the state of the art employing the Channel Features with the algorithm AdaBoost.The second one is the PDM alignment within the bounding box provided by the detection step. Two distinct approaches are presented in this thesis. The first method relies on a parametric formulation to describe the shape, similar to the ASM or AAM. To fit this shape model, we maximize the score of an appearance model defined by GentleBoost, which employs local histograms of oriented gradients. The second approach is based on the cascade regression shape scheme. The main idea is the approximation for each step into a classification of homogeneous deformations, grouped by unsupervised clustering.The third module is the re-identfication one. We show that employing a PDM as a structural support improves re-identification results. We experiment classic appearance-based signatures, color histograms and the shape descriptor Shape Context. The results are encouraging for application perspective of PDM for the gait recognition.