Within the past thirty years, privacy concerns among American citizens are rising with counter-terrorist surveillance going beyond targeting people of interest. These concerns are reflected in American cinema where many contemporary films have explored surveillance in society. The textual analyses presented in the thesis will focus on three such films, Strange Days (1995), Southland Tales (2005), and Nightcrawler (2014). Throughout this thesis, I examine how each of these films offers a unique, reflexive take on surveillance, adhering to generative mechanisms that evoke differing attitudes about surveillance through their form. My analysis draws on Laura Mulvey and Patricia Pisters’ theories on the gaze to understand the politics of looking in contemporary surveillance cinema and highlight how cinematic scopophilia evolved into a networked perspective. My analysis suggests that the politics of surveillance cinema is reflected in these films as their differences mirror the changing perception of surveillance and the gaze over time. / Includes bibliography. / Thesis (M.A.)--Florida Atlantic University, 2016. / FAU Electronic Theses and Dissertations Collection

Cette thèse s’inscrit dans le contexte de l’analyse vidéo du trafic routier. Dans certaines grandes villes, des centaines de caméras produisent de très grandes quantités de données, impossible à manipuler sans traitement automatique. Notre principal objectif est d'aider les opérateurs humains en analysant automatiquement les données vidéo. Pour aider les contrôleurs de la circulation à prendre leurs décisions, il est important de connaître en temps réel, l'état du trafic (nombre de véhicules et vitesse des véhicules sur chaque segment de voie), mais aussi de disposer de statistiques temporelles tout au long de la journée, de la semaine, de la saison ou de l'année. Les caméras ont été déployées depuis longtemps pour le trafic et pour d'autres fins de surveillance, car elles fournissent une source d'information riche pour la compréhension humaine. L'analyse vidéo peut désormais apporter une valeur ajoutée aux caméras en extrayant automatiquement des informations pertinentes. De cette façon, la vision par ordinateur et l'analyse vidéo deviennent de plus en plus importantes pour les systèmes de transport intelligents (intelligent transport systems : ITSs). L’une des problématiques abordées dans cette thèse est liée au comptage automatique de véhicules. Pour être utile, un système de surveillance vidéo doit être entièrement automatique et capable de fournir, en temps réel, l'information qui concerne le comportement de l'objet dans la scène. Nous pouvons obtenir ces renseignements sur la détection et le suivi des objets en mouvement dans les vidéos, ce qui a été un domaine largement étudié. Néanmoins, la plupart des systèmes d'analyse automatique par vidéo ont des difficultés à gérer les situations particulières. Aujourd'hui, il existe de nombreux défis à résoudre tels que les occultations entre les différents objets, les arrêts longs, les changements de luminosité, etc… qui conduisent à des trajectoires incomplètes. Dans la chaîne de traitements que nous proposons, nous nous sommes concentrés sur l'extraction automatique de statistiques globales dans les scènes de vidéosurveillance routière. Notre chaîne de traitements est constituée par les étapes suivantes : premièrement, nous avons évalué différentes techniques de segmentation de vidéos et de détection d'objets en mouvement. Nous avons choisi une méthode de segmentation basée sur une version paramétrique du mélange de gaussiennes appliquée sur une hiérarchie de blocs, méthode qui est considérée actuellement comme l'un des meilleurs procédés pour la détection d'objets en mouvement. Nous avons proposé une nouvelle méthodologie pour choisir les valeurs optimales des paramètres d’un algorithme permettant d’améliorer la segmentation d’objets en utilisant des opérations morphologiques. Nous nous sommes intéressés aux différents critères permettant d’évaluer la qualité d’une segmentation, résultant d’un compromis entre une bonne détection des objets en mouvement, et un faible nombre de fausses détections, par exemple causées par des changements d’illumination, des reflets ou des bruits d’acquisition. Deuxièmement, nous effectuons une classification des objets, basée sur les descripteurs de Fourier, et nous utilisons ces descripteurs pour éliminer les objets de type piétons ou autres et ne conserver que les véhicules. Troisièmement, nous utilisons un modèle de mouvement et un descripteur basé sur les couleurs dominantes pour effectuer le suivi des objets extraits. En raison des difficultés mentionnées ci-dessus, nous obtenons des trajectoires incomplètes, qui donneraient une information de comptage erronée si elles étaient exploitées directement. Nous proposons donc d’agréger les données partielles des trajectoires incomplètes et de construire une information globale sur la circulation des véhicules dans la scène. Notre approche permet la détection des points d’entrée et de sortie dans les séquences d’images. Nous avons testé nos algorithmes sur des données privées provenant... / This thesis is written in the context of video traffic analysis. In several big cities, hundreds of cameras produce very large amounts of data, impossible to handle without automatic processing. Our main goal is to help human operators by automatically analyzing video data. To help traffic controllers make decisions, it is important to know the traffic status in real time (number of vehicles and vehicle speed on each path), but also to dispose of traffic statistics along the day, week, season or year. The cameras have been deployed for a long time for traffic and other monitoring purposes, because they provide a rich source of information for human comprehension. Video analysis can automatically extract relevant information. Computer vision and video analysis are becoming more and more important for Intelligent Transport Systems (ITSs). One of the issues addressed in this thesis is related to automatic vehicle counting. In order to be useful, a video surveillance system must be fully automatic and capable of providing, in real time, information concerning the behavior of the objects in the scene. We can get this information by detection and tracking of moving objects in videos, a widely studied field. However, most automated video analysis systems do not easily manage particular situations.Today, there are many challenges to be solved, such as occlusions between different objects, long stops of an object in the scene, luminosity changes, etc., leading to incomplete trajectories of moving objects detected in the scene. We have concentrated our work on the automatic extraction of global statistics in the scenes. Our workflow consists of the following steps: first, we evaluated different methods of video segmentation and detection of moving objects. We have chosen a segmentation method based on a parametric version of the Mixture of Gaussians, applied to a hierarchy of blocks, which is currently considered one of the best methods for the detection of moving objects. We proposed a new methodology to choose the optimal parameter values of an algorithm to improve object segmentation by using morphological operations. We were interested in the different criteria for evaluating the segmentation quality, resulting from a compromise between a good detection of moving objects, and a low number of false detections, for example caused by illumination changes, reflections or acquisition noises. Secondly, we performed an objects classification, based on Fourier descriptors, and we use these descriptors to eliminate pedestrian or other objects and retain only vehicles. Third, we use a motion model and a descriptor based on the dominant colors to track the extracted objects. Because of the difficulties mentioned above, we obtain incomplete trajectories, which, exploited as they are, give incorrect counting information. We therefore proposed to aggregate the partial data of the incomplete trajectories and to construct a global information on the vehicles circulation in the scene. Our approach allows to detect input and output points in image sequences. We tested our algorithms on private data from the traffic control center in Chiang Mai City, Thailand, as well as on MIT public video data. On this last dataset, we compared the performance of our algorithms with previously published articles using the same data. In several situations, we illustrate the improvements made by our method in terms of location of input / output zones, and in terms of vehicle counting.

A violência urbana e o medo globalizado, marcas do atual período técnico-científico e informacional, têm alterado as paisagens de diversas partes do mundo através do processo denominado neste trabalho pelo termo securização urbana. Como resposta à sensação de insegurança e de imprevisibilidade, busca-se uma racionalização do território a partir da informatização do cotidiano e da criação de espaços exclusivos. Empiricamente, a securização se traduz em formas arquitetônicas variadas, tendo sido destacadas nesta tese as câmeras de vigilância, os condomínios fechados e as arquiteturas anti-indesejáveis. Transformações espaciais como essas são particularmente intensas em Campinas-SP, município brasileiro ao mesmo tempo muito rico, com importantes empresas e universidades, e muito pobre e violento, portando índices de criminalidade acima da média nacional. Exemplos desse e também de outros lugares do Brasil e da Europa foram analisados em trabalhos de campo que contaram com entrevistas colhidas de agentes locais, fotografias e cartografias, com o intuito de caracterizar o processo de securização e, especialmente, conduzir a uma reflexão sobre suas conseqüências. Conclui-se que a maneira pela qual a segurança tem sido buscada aumenta as desigualdades espaciais e promove uma privatização dos espaços públicos. Além disso, o excesso de vigilância tem reforçado a psicoesfera do medo, tolhido muitas das liberdades individuais e criado novas neuroses e violências. A racionalização do espaço para fins de segurança cria, contudo, as condições para o surgimento de contra-racionalidades, o que reafirma o caráter complexo e dialético do espaço geográfico e aponta para a possibilidade de um futuro marcado pelas solidariedades geográficas e pelo poder revolucionário dos agentes não-hegemônicos. / Urban violence and globalized fear, hallmarks of the current technical-scientific and informational period, have transformed the landscape of different cities of the world through a process called urban securitization\". In response to the feeling of insecurity and unpredictability, territories are being rationalised through the digitalization of everyday life and creation of exclusive areas. Empirically, securitization can be materialized through different architectural forms. This thesis highlights surveillance cameras, gated communities and anti-beggars architectures. These transformations are particularly intense in Campinas, a very wealthy Brazilian city, with important companies and universities, and at the same time very poor and violent, with crime rates above the national average. Examples from this city, but also from other parts of Brazil and Europe, were analyzed in field works which involved interviews with local agents, photos and maps, in order to describe the process of securitization and, especially, to lead to a reflection of their consequences. In conclusion, it can be stated that the way security is being searched increases spatial inequalities and promotes a privatization of public spaces. Furthermore, an excessive surveillance has enhanced the psycho-sphere of fear, has restraint individual liberties and has produced new neuroses and violence. However, rationalisation of space for security purposes facilitates the emergence of counter-rationalities, emphasizing the complex and dialectic qualities of geographic space and indicating the possibility of a future characterized by geographic solidarities and by revolutionary power of non-hegemonic agents.

The nearshore waters off the Oregon coast (< 73 meters) are a region of high productivity and economic value, with a variety of habitats that include rock outcrops. Temperate reef habitats are important to many commercially important fishes inhabiting the Pacific coast, including canary rockfish (Sebastes pinniger) and yelloweye rockfish (Sebastes ruberrimus), which are currently listed as "overfished" by the Pacific Fishery Management Council. Along the Pacific coast of North America, nearshore rocky reefs have been designated as essential fish habitat (EFH), while comprising approximately just seven percent of Oregon's territorial sea. Despite this EFH designation, the use of visual (SCUBA, remotely operated vehicles (ROVs), human occupied vehicles (HOVs)) and non-visual (bottom trawl) survey methods within this region has been infrequent and scattered, providing limited information on species-habitat associations and species assemblages within nearshore waters. It is logistically difficult and costly to survey nearshore reefs. The factors that have led to the paucity of surveys include the depth (too deep for SCUBA surveys but too shallow for larger survey vessels), high seas limiting available days for field work, and the high-relief nature of the habitat (precluding the use of bottom trawls). In an effort to better understand species-habitat associations and community structure of Oregon's nearshore reefs, an autonomous underwater drop-camera termed the "video lander" was employed at the Three Arch Rocks reef, a nearshore reef off of Oceanside, Oregon. Video lander footage was used to identify and groundtruth habitat types, as well as species assemblages over two distinct seasons: spring/summer (n=272) and winter (n=108). Many species-habitat associations were statistically significant: yelloweye rockfish (large boulder p<0.0073), canary rockfish (small boulder p<0.0006), kelp greenling (Hexagrammos decagrammus) (bedrock outcrop p<0.0162), and quillback rockfish (Sebastes maliger) (large boulder p<0.0016). Summer and winter surveys revealed similar habitat associations and distributions for these species. I found no significant difference in species composition between the northern and southern regions of the reef (Bray-Curtis dissimilarity index (BCDI) = 71.71, ANOSIM p>0.1447), but a significant difference between spring/summer and winter seasons was identified on the outer section of the reef, due to the presence of spotted ratfish (Hydrolagus colliei) in the winter (BCDI =76.41, ANOSIM p < 0.0155). My study shows that data provided by the video lander can fill existing gaps in our understanding of nearshore distribution and habitat associations of temperate rocky-reef fishes off the Oregon coast. / Graduation date: 2013

Analysis and visualization of dynamic scenes is often constrained by the amount of spatio-temporal information available from the environment. In most scenarios, we have to account for incomplete information and sparse motion data, requiring us to employ interpolation and approximation methods to fill for the missing information. Scattered data interpolation and approximation techniques have been widely used for solving the problem of completing surfaces and images with incomplete input data. We introduce approaches for such data interpolation and approximation from limited sensors, into the domain of analyzing and visualizing dynamic scenes. Data from dynamic scenes is subject to constraints due to the spatial layout of the scene and/or the configurations of video cameras in use. Such constraints include: (1) sparsely available cameras observing the scene, (2) limited field of view provided by the cameras in use, (3) incomplete motion at a specific moment, and (4) varying frame rates due to different exposures and resolutions. In this thesis, we establish these forms of incompleteness in the scene, as spatio-temporal uncertainties, and propose solutions for resolving the uncertainties by applying scattered data approximation into a spatio-temporal domain. The main contributions of this research are as follows: First, we provide an efficient framework to visualize large-scale dynamic scenes from distributed static videos. Second, we adopt Radial Basis Function (RBF) interpolation to the spatio-temporal domain to generate global motion tendency. The tendency, represented by a dense flow field, is used to optimally pan and tilt a video camera. Third, we propose a method to represent motion trajectories using stochastic vector fields. Gaussian Process Regression (GPR) is used to generate a dense vector field and the certainty of each vector in the field. The generated stochastic fields are used for recognizing motion patterns under varying frame-rate and incompleteness of the input videos. Fourth, we also show that the stochastic representation of vector field can also be used for modeling global tendency to detect the region of interests in dynamic scenes with camera motion. We evaluate and demonstrate our approaches in several applications for visualizing virtual cities, automating sports broadcasting, and recognizing traffic patterns in surveillance videos.

This dissertation presents an efficient multilayer background modeling approach to distinguish among midground objects, the objects whose existence occurs over varying time scales between the extremes of short-term ephemeral appearances (foreground) and long-term stationary persistences (background). Traditional background modeling separates a given scene into foreground and background regions. However, the real world can be much more complex than this simple classification, and object appearance events often occur over varying time scales. There are situations in which objects appear on the scene at different points in time and become stationary; these objects can get occluded by one another, and can change positions or be removed from the scene. Inability to deal with such scenarios involving midground objects results in errors, such as ghost objects, miss-detection of occluding objects, aliasing caused by the objects that have left the scene but are not removed from the model, and new objects’ detection when existing objects are displaced. Modeling temporal layers of multiple objects allows us to overcome these errors, and enables the surveillance and summarization of scenes containing multiple midground objects.

「家」是人類生活的重心，居家影像監控設備的需求來自於家庭用戶想要掌握居家環境安全現況。相對於商業影像監控的應用，居家影像監控設備安裝的複雜度需要較低。因此居家影像監控市場以簡易安裝、遠端監控為主要的發展趨勢。 網路攝影機(IP Camera)具有數位化及網路化的特色，掌握寬頻網路的電信業者積極為影像監控市場創造新興的居家應用情境。許多傳統影像監控產品製造商為因應居家影像監控的潮流，也紛紛推出產品因應。然而電信業者及傳統影像監控業者在居家影像監控的市場都沒有得到很大的成功。個案公司創立於2009年，在2012年所推出的第一代產品，一開始只在網路上銷售，卻在2012、2013席捲美國市場，成為美國居家影像監控產品的第一品牌。 本研究探討之研究問題包括: (一)數位化及網際網路的普及對影像監控產品設計之影響、(二)行動裝置的普及對影像監控產品設計之影響、(三)個案公司提升消費者認知價值之產品創新策略。 本研究採用個案研究方法，首先列出客戶所重視的產品要素，以產品關鍵要素為橫軸客戶對個案產品的認知價值為縱軸描繪出曲線圖，比較個案公司產品與競爭對手的產品價值曲線，可解讀出其策略上的差異點。最後運用藍海策略思維，重塑和創新價值曲線以產生新的策略意義。 本研究發現，數位科技進步及網際網路的普及降低了使用影像監控產品的門檻，因而使居家影像監控產品得以被大量採用。而移動裝置的普及改變了消費者使用影像監控產品的方式，使用者的體驗主要來自於手機App的操作經驗，與過去在個人電腦上的使用經驗完全不同。個案公司以解決使用者痛點作為提升消費者認知價值之產品創新策略，值得居家影像監控業者未來產品規劃參考。 / Home is the center of human life, home video surveillance equipment demand from home users want to master the security status of the home environment. With respect to the commercial video surveillance applications, home video surveillance equipment requires low complexity of the installation. Therefore, the main trend of home video surveillance is easy installation and remote monitoring. IP Camera has digitized and network-oriented features, Internet carriers create new application scenarios for home video surveillance market. Many traditional video surveillance equipment manufacturers to cope with the trend of home video surveillance have also introduced products in response. However, carriers and traditional video surveillance industry in the home video surveillance markets have not been very successful. Founded in 2009, Dropcam Inc. launched the first generation of products in 2012, selling only on the Internet at the beginning, became the top brand of home video surveillance products in United States in 2013. This study try to answer three questions: (1) The popularity of digital technology and the Internet's impact on the design of video surveillance products, (2) The impact of the popularity of mobile devices on the design of video surveillance products, (3) Dropcam’s product innovation strategy to enhance consumer perceived value of the product. This study used a case study approach. Comparing the product value curve of Dropcam's products and its competitor’s product can interpret difference of strategy. Finally, the use of blue ocean strategy thinking, remodeling and innovation to create a new value curve strategic importance. The study found that the popularity of digital technology and the Internet's progress reducing the threshold to use of video surveillance products, thus making home video surveillance products to be widely adopted. The popularity of mobile devices has changed the way consumers use video surveillance products. Dropcam to resolve user’s pain points to enhance consumer awareness of the value of a product. It is worth for coming home video surveillance maker’s reference.

Pedestrian movement is unconstrained. For this reason it is not amenable to mathematical modelling in the same way as road trac. Individual pedestrians are notoriously difficult to monitor at a microscopic level. This has led to a lack of primary data that can be used to develop reliable models. Although video surveillance is cheap to install and operate, video data is extremely expensive to process for this purpose. An alternative approach is to use passive infrared detectors that are able to track individuals unobtrusively. This thesis describesthe use of a low cost infrared sensor for use in tracking pedestrians. The sensor itself, manufactured by a British company, is designed to count people crossing an arbitrary datum line. However, with the development of additional software, the functionality of these sensors can be extended beyond their original design specication. This allows the trajectories of individual pedestrians to be tracked. Although the field of view of each sensor is relatively small (44 m), five were deployed in a busy indoor corridor, covering most of its length. In this research, the technical challenges involved in using the sensors in this way are addressed. Statistics derived from the data collected are then compared to other studies at this scale.

Cette thèse présente l'utilisation conjointe de la théorie de l'évidente et du suivi multi-hypothèses pour la modélisation et la gestion d'un système de suivi multi-caméras dans un environnement autoroutier. Le suivi est basé sur la ré-identification des objets (véhicules) sur la base d'information visio-temporelles. Une concrétisation de ces concepts se traduit par la conception et la mise en oeuvre d'une architecture logicielle multi-agents de gestion du suivi multi-caméras. Après une présentation de l'état de l'art sur les cadres de gestion de l'incertain et celui relatif à fusion de l'information pour la mise en correspondance, et sur les systèmes multi-agents, notre apport dans ce travail se situe à trois niveaux. Le premier a été une adaptation de la phase de décision du modèle de croyance transférable pour y intégrer l'utilisation du suivi multi-hypothèses comme outil de levée d'ambigüité rn cas d'indécision face à une situation de mise en correspondance. Le second apport a été celui de proposer une architecture logicielle à base d'agents pour la gestion du système du suivi multi-caméras. Nous en avons proposé la modélisation globale ainsi que celle des agents et de leurs interactions en utilisant une démarche personnelle d'analyse mais toutefois inspirée de langages et outils de modélisation tels que Agent UML et MaSE pour ne citer que ceux-là, du fait qu'il n'existe pas réellement un standard normalisé à ce jour dans ce domaine. Notre troisième apport a été de faire un début d'implémentation de notre architecture logicielle à base d'agent en nous basant sur la plateforme JADE (Java Agent DEvelopment Framework). Quelques expérimentations et discussions des résultats sont présentées à la fin pour déboucher sur nos conclusions et perspectives. / This thesis presents the joint use of the theory of evidence and multiple hypothesis tracking for modeling and managing a system for monitoring multiple cameras in a motorway. The tracking is based on the re-identification of objects (vehicles) on the basis of visuals and times informations. A realization of these concepts results in the design and implementation of a software architecture for multiple agents management of multiple camera tracking system. After presenting the state of the art on the frameworks of uncertainty management and that on information fusion for the matching, and the multi-agent systems, our contribution in this work is on two or three levels. The first was an adaptation of the decision phase of the transferable belief model to incorporate the use of multi-hypotheses tracking as a tool of ambiguity survey in case of indecision in matching situation. The second contribution was a proposition of agent-based software architecture for management of a multiple cameras tracking system. We have proposed the global system modeling as well as agents and their interactions modeling using a personal analysis method but nevertheless inspired by modelisation languages and tolls such as Agent UML, MaSE and others, because there is not yet a standard and normalized tool on the subject. Our third contribution was to begin an implementation of our agent-based software architecture using JADE (Java Agent Development Framework). Some experiment and discussions are presented at the end to lead to our conclusions and perspectives.

La vision artificielle est un domaine de recherche en pleine évolution. Les nouvelles stratégies permettent d'avoir des réseaux de caméras intelligentes. Cela induit le développement de beaucoup d'applications de surveillance automatique via les caméras. Les travaux développés dans cette thèse concernent la mise en place d'un système de vidéosurveillance intelligente pour la détection de chutes en temps réel. La première partie de nos travaux consiste à pouvoir estimer de façon robuste la surface d'une personne à partir de deux (02) caméras ayant des vues complémentaires. Cette estimation est issue de la détection de chaque caméra. Dans l'optique d'avoir une détection robuste, nous avons fait recours à deux approches. La première approche consiste à combiner un algorithme de détection de mouvements basé sur la modélisation de l'arrière plan avec un algorithme de détection de contours. Une approche de fusion a été proposée pour rendre beaucoup plus efficiente le résultat de la détection. La seconde approche est basée sur les régions homogènes de l'image. Une première ségmentation est effectuée dans le but de déterminer les régions homogènes de l'image. Et pour finir, nous faisons la modélisation de l'arrière plan en se basant sur les régions. Une fois les pixels de premier plan obtenu, nous faisons une approximation par un polygone dans le but de réduire le nombre d'informations à manipuler. Pour l'estimation de cette surface nous avons proposé une stratégie de fusion dans le but d'agréger les détections des caméras. Cette stratégie conduit à déterminer l'intersection de la projection des divers polygones dans le plan de masse. La projection est basée sur les principes de l'homographie planaire. Une fois l'estimation obtenue, nous avons proposé une stratégie pour détecter les chutes de personnes. Notre approche permet aussi d'avoir une information précise sur les différentes postures de l'individu. Les divers algorithmes proposés ont été implémentés et testés sur des banques de données publiques dans le but de juger l'efficacité des approches proposées par rapport aux approches existantes dans l'état de l'art. Les résultats obtenus et qui ont été détaillés dans le présent manuscrit montrent l'apport de nos algorithmes. / Artificial vision is an involving field of research. The new strategies make it possible to have some autonomous networks of cameras. This leads to the development of many automatic surveillance applications using the cameras. The work developed in this thesis concerns the setting up of an intelligent video surveillance system for real-time people fall detection. The first part of our work consists of a robust estimation of the surface area of a person from two (02) cameras with complementary views. This estimation is based on the detection of each camera. In order to have a robust detection, we propose two approaches. The first approach consists in combining a motion detection algorithm based on the background modeling with an edge detection algorithm. A fusion approach has been proposed to make much more efficient the results of the detection. The second approach is based on the homogeneous regions of the image. A first segmentation is performed to find homogeneous regions of the image. And finally we model the background using obtained regions.