With facial recognition technology becoming a greater part of our everyday lives the ethicalimplications it may bring is something worth exploring, and according to scientists the technology could already be incorporated in video surveillance. Previous studies have shown thatwomen and men see things differently within this context, something that will be explored inthis paper. Through focus group interviews and a survey study the students could not confirmthis statement, and found that in only one specific context women and men had significantdifferent standpoints. Further the study found that there is a fear of what this technology couldresult in, in the form of abuse of the information gathered about people and how society couldchange with the incorporation of said technology in video surveillance. / Eftersom ansiktsigenkänningsteknologi blir en större del av vår vardag är de etiska konsekvenserna det kan ge något som är värt att utforska, och enligt forskare kan tekniken redanfinnas implementerad i videoövervakning. Tidigare studier har visat att kvinnor och män uppfattar saker annorlunda inom detta sammanhang, något som kommer att undersökas i dennauppsats. Genom fokusgruppsintervjuer och en enkätstudie kunde studenterna inte bekräftadetta uttalande och fann endast att kvinnor och män i ett specifikt sammanhang hade märkbartolika uppfattningar. Vidare fann studien att det finns en rädsla för vad denna teknik kan resultera i, i form av missbruk av den information som samlats in om människor och hur samhälletkan förändras genom införandet av nämnd teknik i videoövervakning.

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Bürger weisen eine positive Einstellung gegenüber Videoüberwachung auf, obwohl sie sehr wenig über Videoüberwachung nachdenken, wenig über die Zahl und Verteilung der Videokameras in ihrer Stadt wissen, Videoüberwachung nicht mit ihren Bürgerrechten in Beziehung bringen sowie dem Staat „blind\\\\\\\\\\\\\\\" vertrauen. Klocke resümiert: Das Unwissen über die Kamerawirklichkeit ist als ein Anzeichen für bürgerrechtliche Unmotiviertheit und mangelnde Freiheitssensibilität anzusehen. Daraus ergibt sich die Forschungsfrage dieses Aufsatzes, welche darauf abzielt nicht die Einstellung zur Videoüberwachung, sondern die (geringe) Aufmerksamkeit gegenüber Videoüberwachung zu erklären: Warum sind Menschen gleichgültig gegenüber Videoüberwachung, obwohl dadurch eines ihrer Grundrechte beeinträchtigt wird?:Vorbemerkung; Fragestellung; Theoretischer Ansatz und Annahmen; Daten und Operationalisierung; Ergebnisse; Zusammenfassung und Diskussion der Ergebnisse; Ausblick

With the increasing demand of intelligent video surveillance systems, person re-identification (re-ID) plays an important role in intelligent video analysis, which aims at matching person images across non-overlapping camera views. It has gained increasing attention in computer vision community. With the advanced deep neural networks, existing methods have achieved promising performance on the widely-used re-ID benchmarks, even outperform the human-level rank-1 matching accuracy. However, most of the research efforts are conducted on the closed-world settings, with large-scale well annotated training data and all the person images are from the same visible modality. As a prerequisite in practical video surveillance application, there is still a large gap between the closed-world research-oriented setting and the practical open-world settings. In this thesis, we try to narrow the gap by studying three important issues in open-world person re-identification, including 1) unsupervised learning with large-scale unlabelled training data; 2) learning robust re-ID model with label corrupted training data and 3) cross-modality visible-thermal person re-identification with multi-modality data. For unsupervised learning with unlabelled training data, we mainly focus on video-based person re-identification, since the video data is usually easily obtained by tracking algorithms and the video sequence provides rich weakly labelled samples by assuming the image frames within the tracked sequence belonging to the same person identity. Following the cross-camera label estimation approach, we formulate the cross-camera label estimation as a one-to-one graph matching problem, and then propose a novel dynamic graph matching framework to estimate cross-camera labels. However, in a practical wild scenario, the unlabelled training data usually cannot satisfy the one-to-one matching constraint, which would result in a large proportion of false positives. To address this issue, we further propose a novel robust anchor embedding method for unsupervised video re-ID. In the proposed method, some anchor sequences are firstly selected to initialize the CNN feature representation. Then a robust anchor embedding method is proposed to measure the relationship between the unlabelled sequences and anchor sequences, which considers both the scalability and efficiency. After that, a top-{dollar}k{dollar} counts label prediction strategy is proposed to predict the labels of unlabelled sequences. With the newly estimated sequences, the CNN representation could be further updated. For robust re-ID model learning with label corrupted training data, we propose a two-stage learning method to handle the label noise. Rather than simply filtering the falsely annotated samples, we propose a joint learning method by simultaneously refining the falsely annotated labels and optimizing the neural networks. To address the limited training samples for each identity, we further propose a novel hard-aware instance re-weighting strategy to fine-tune the learned model, which assigns larger weights to hard samples with correct labels. For cross-modality visible-thermal person re-identification, it addresses an important issue in night-time surveillance applications by matching person images across different modalities. We propose a dual-path network to learn the cross-modality feature representations, which learns the multi-modality sharable feature representations by simultaneously considering the modality discrepancy and commonness. To guide the feature representation learning process, we propose a dual-constrained top-ranking loss, which contains both cross-modality and intra-modality top-ranking constraints to reduce the large cross-modality and intra-modality variations. Besides the open-world person re-identification, we have also studied the unsupervised embedding learning problem for general image classification and retrieval. Motivated by supervised embedding learning, we propose a data augmentation invariant and instance spread-out feature. To learn the feature embedding, we propose a instance feature-based softmax embedding, which optimizes the embedding directly on top of the real-time instance features. It achieves much faster learning speed and better accuracy than existing methods. In short, the major contributions of this thesis are summarized as follows. l A dynamic graph matching framework is proposed to estimate cross-camera labels for unsupervised video-based person re-identification. l A robust anchor embedding method with top-{dollar}k{dollar} counts label prediction is proposed to efficiently estimate the cross-camera labels for unsupervised video-based person re-identification under wild settings. l A two-stage PurifyNet is introduced to handle the label noise problem in person re-identification, which jointly refines the falsely annotated labels and mines hard samples with correct labels. l A dual-constrained top-ranking loss with a dual-path network is proposed for cross-modality visible-thermal person re-identification, which simultaneously addresses the cross-modality and intra-modality variations. l A data augmentation invariant and instance spread-out feature is proposed for unsupervised embedding learning, which directly optimizes the learned embedding on top of real-time instance features with softmax function

Désormais, l'analyse des scènes denses s'impose incontestablement comme une tâche importante pour contrôler et gérer les foules. Notre recherche a pour objectifs d'apporter des solutions à l'estimation de la densité de la foule et de prouver l'utilité de cette estimation comme préalable pour d'autres applications. Concernant le premier objectif, afin de cerner les difficultés de la détection de personnes dans une foule, on se focalise sur l'estimation de la densité basée sur un niveau d'analyse bas. Dans un premier temps, on démontre que nos approches sont plus adéquates que les méthodes de l’état de l’art que ce soit pour compter les individus ou pour estimer le niveau de la foule. Dans un second temps, nous proposons une approche innovante dans laquelle une estimation locale au niveau des pixels remplace l'estimation au niveau global de la foule ou le nombre des personnes. Elle est basée sur l’utilisation des suivis de caractéristiques visuelles dans une fonction de densité. Notre recherche a également pour objectif d'utiliser la densité comme information supplémentaire pour affiner d'autres tâches. D'abord, nous avons utilisé la mesure de la densité qui comporte une description pertinente à la répartition spatiale des individus afin d'améliorer leur détection et leur suivi dans les foules. Ensuite, en prenant en compte la notion de la protection de la vie privée, nous ajustons le niveau de floutage en fonction de la densité de la foule. Enfin, nous nous appuyons sur l’estimation locale de la densité ainsi que sur le mouvement en tant qu'attributs pour des applications de haut niveau telles que la détection des évolutions et la reconnaissance des événements. / Crowd analysis has recently emerged as an increasingly important problem for crowd monitoring and management in the visual surveillance community. In this thesis, our objectives are to address the problems of crowd density estimation and to investigate the usefulness of such estimation as additional information to other applications. Towards the first goal, we focus on the problems related to the estimation of the crowd density using low level features in order to avert typical problems in detection of high density crowd. We demonstrate in this dissertation, that the proposed approaches perform better than the baseline methods, either for counting people, or alternatively for estimating the crowd level. Afterwards, we propose a novel approach, in which local information at the pixel level substitutes the overall crowd level or person count. It is based on modeling time-varying dynamics of the crowd density using sparse feature tracks as observations of a probabilistic density function. The second goal is to use crowd density as additional information to complement other tasks related to video surveillance in crowds. First, we use the proposed crowd density measure which conveys rich information about the local distributions of persons to improve human detection and tracking in videos of high density crowds. Second, we investigate the concept of crowd context-aware privacy protection by adjusting the obfuscation level according to the crowd density. Finally, we employ additional information about the local density together with regular motion patterns as crowd attributes for high level applications such as crowd change detection and event recognition.

La problématique de l’observance de la biosécurité est présente dans tous les types de production. Il est essentiel de définir des stratégies pour améliorer l’application des mesures de biosécurité. Cette étude décrit l’application des mesures de biosécurité à l’entrée et à la sortie de 24 bâtiments d’élevages avicoles au Québec, Canada. L’effet des audits et de caméras visibles sur l’observance a été étudié, de même que les déterminants de l’observance. De plus, la relation entre l’observance et les profils de personnalité, l’expérience et l’éducation a été décrite. L’application des mesures de biosécurité a été évaluée à l’aide de caméras cachées. L’observance à court terme (deux premières semaines) et à moyen terme (six mois plus tard) a été déterminée. Basés sur les résultats du groupe contrôle, 44 différentes erreurs lors de l’application des mesures de biosécurité ont été observées à l’entrée et la sortie des bâtiments. La plupart étaient reliées à la délimitation des zones (propre versus contaminée). La nature et la fréquence des erreurs suggèrent un manque de compréhension des principes associés aux mesures de biosécurité. Le visionnement des vidéos a révélé 3055 visites par 277 individus différents (136 employés, 123 visiteurs, 3 superviseurs et 15 éleveurs). Les résultats ont démontré que les audits n’avaient pas d’impact sur l’observance des employés. Les caméras visibles ont eu un impact, à court terme, sur le port de bottes et le respect des zones durant la visite. Par contre, six mois plus tard, l’observance avait significativement diminué, au point de ne plus être statistiquement plus élevée que le groupe contrôle. La durée et le moment de la visite, la présence de l’éleveur ou d’un observateur, la conception de l’entrée, le nombre de bâtiments, le nombre de mesures de biosécurité exigé, le type de bottes, le genre et être membre de la famille de l’éleveur étaient significativement associés à l’observance de certaines mesures. Finalement, trois traits de la personnalité étaient associés à l’observance: responsabilité, orienté vers l’action et complexité, de même que le nombre d’années d’expérience et le niveau d’éducation. Il est nécessaire d’améliorer la formation en matière de biosécurité en fournissant du matériel de formation à tous les intervenants qui démontrent pourquoi et comment appliquer les mesures de biosécurité. La formation continue devrait également aborder les problématiques reliées aux caractéristiques de visites et de fermes. Améliorer la conception des entrées de bâtiments devrait contribuer à augmenter et à maintenir l’observance. L’identification de traits de personnalité associés à l’observance peut avoir des implications sur la sélection des candidats à l’embauche ou sur l’attribution de tâches et sur la planification des programmes de formation. / Biosecurity compliance issue is present in all types of animal productions, Therefore, it is essential to define strategies to improve the implementation of biosecurity measures. This study described the application of biosecurity measures when entering and exiting 24 poultry barns in Québec, Canada. The effect of audits and visible cameras on compliance was investigated, as well as determinants of compliance. Also, the relationship between compliance and personality profiles, experience and education has been described. Application of biosecurity measures was evaluated using hidden cameras. Short term (first two weeks) and medium term (six months later) compliance were determined. Based on the control group, 44 different biosecurity breaches were observed when getting in and out of poultry barns. Most were related to area delimitation (clean versus contaminated). The nature and frequency of errors suggest a lack of understanding of biosecurity principles. Overall, video viewing revealed 3055 visits done by 277 different individuals (136 employees, 123 visitors, 3 supervisors and 15 growers). Results showed that audits did not have any impact on employee compliance. Visible cameras had a significant impact on changing boots and respecting areas during the visit for the short term period. However, six months later, compliance significantly declined and was no longer statistically higher compared to the control group. Duration and moment of the visit, presence of the grower or an observer, barn entrance design, number of barns, number of biosecurity measures requested, type of boots, gender and being a member of a grower’s family were significantly associated with compliance with some biosecurity measures. Finally, three personality traits were significantly associated with compliance: responsibility, action-oriented and complexity, as well as the number of years of experience and the level of education. There is a need to improve biosecurity training by making training material available to all poultry personnel demonstrating why and how to apply biosecurity measures. Educational meetings should also address issues related to visit and farm characteristics. Improving barn entrance design should contribute to enhance and maintain compliance. The identification of personality traits associated with compliance may have implications for the selection of job applicants or task attribution, and for developing educational materials and training programs.

Cette thèse est effectuée en collaboration entre le LaBRI (Laboratoire bordelais de recherche en informatique) et MIRANE S.A.S., le leader français en Publicité sur Lieu de Vente (PLV) Dynamique. Notre but est d'analyser des comportements humains dans un point de vente. Le long de cette thèse, nous présentons un système d'analyse vidéo composé de plusieurs procédés de divers niveaux. Nous présentons, dans un premier temps, l'analyse vidéo de bas niveau composée de la détection de mouvement et du suivi d'objets. Puis nous analysons le comportement de ces objets suivis, lors de l'analyse de niveau moyen. Finalement, l'analyse de haut-niveau est composée d'une interprétation sémantique de ces comportements et d'une détection de scenarios de haut-niveau. / Along this thesis various subjects are studied, from the lowest to the higher level of video analysis. We first present motion detection and object tracking that compose the low-level processing part of our system. Motion detection aims at detecting moving areas, which correspond to foreground, of an image. The result of motion detection is a foreground mask that is used as input for the object tracking process. Tracking matches and identifies foreground regions across frames. Then, we analyze the behavior of the tracked objects, as the mid-level analysis. At each frame, we detect the current state of action of each tracked object currently in the scene. Finally, the system generates a semantic interpretation of these behaviors and we analyze high-level scenarios as the high-level part of our system. These two processes analyze the series of states of each object. The semantic interpretation generates sentences when state changes occur. Scenario recognition detect three different scenarios by analyzing the temporal constraints between the states.

[pt] Esta dissertação teve por objetivo comparar o desempenho de diversos algoritmos que representam o estado da arte em reconhecimento facial a imagens de sequências de vídeo. Três objetivos específicos foram perseguidos: desenvolver um método para determinar quando uma face está em posição frontal com respeito à câmera (detector de face frontal); avaliar a acurácia dos algoritmos de reconhecimento com base nas imagens faciais obtidas com ajuda do detector de face frontal; e, finalmente, identificar o algoritmo com melhor desempenho quando aplicado a tarefas de verificação e identificação. A comparação dos métodos de reconhecimento foi realizada adotando a seguinte metodologia: primeiro, foi criado um detector de face frontal que permitiu o captura das imagens faciais frontais; segundo, os algoritmos foram treinados e testados com a ajuda do facereclib, uma biblioteca desenvolvida pelo Grupo de Biometria no Instituto de Pesquisa IDIAP; terceiro, baseando-se nas curvas ROC e CMC como métricas, compararam-se os algoritmos de reconhecimento; e por ultimo, as análises dos resultados foram realizadas e as conclusões estão relatadas neste trabalho. Experimentos realizados sobre os bancos de vídeo: MOBIO, ChokePOINT, VidTIMIT, HONDA, e quatro fragmentos de diversos filmes, indicam que o Inter Session Variability Modeling e Gaussian Mixture Model são os algoritmos que fornecem a melhor acurácia quando são usados em tarefas tanto de verificação quanto de identificação, o que os indica como técnicas de reconhecimento viáveis para o vídeo monitoramento automático em vídeo. / [en] This dissertation aimed to compare the performance of state-of-the-arte face recognition algorithms in facial images captured from multiple video sequences. Three specific objectives were pursued: to develop a method for determining when a face is in frontal position with respect to the camera (frontal face detector); to evaluate the accuracy for recognition algorithms based on the facial images obtained with the help of the frontal face detector; and finally, to identify the algorithm with better performance when applied to verification and identification tasks in video surveillance systems. The comparison of the recognition methods was performed adopting the following approach: first, a frontal face detector, which allowed the capture of facial images was created; second, the algorithms were trained and tested with the help of facereclib, a library developed by the Biometrics Group at the IDIAP Research Institute; third, ROC and CMC curves were used as metrics to compare the recognition algorithms; and finally, the results were analyzed and the conclusions were reported in this manuscript. Experiments conducted on the video datasets: MOBIO, ChokePOINT, VidTIMIT, HONDA, and four fragments of several films, indicate that the Inter-Session Variability Modelling and Gaussian Mixture Model algorithms provide the best accuracy on classification when the algorithms are used in verification and identification tasks, which indicates them as a good automatic recognition techniques for video surveillance applications.

Criminals often resort to camera tampering to prevent capture of their actions. Many surveillance systems left unattended and videos surveillance system operators lose their concentration after a short period of time. Many important Real-time automated detection of video camera tampering cases is important for timely warning of the operators. Tampering can be defined as deliberate physical actions on a video surveillance camera and is generally done by obstructing the camera view by a foreign object, displacing the camera and changing the focus of the camera lens. In automated camera tamper detection systems, low false alarm rates are important as reliability of these systems is compromised by unnecessary alarms and consequently the operators start ignoring the warnings. We propose adaptive algorithms to detect and identify such cases with low false alarms rates in typical surveillance scenarios where there is significant activity in the scene. We also give brief information about the camera tampering detection algorithms in the literature. In this thesis we compare performance of the proposed algorithms to the algorithms in the literature by experimenting them with a set of test videos.

Title from PDF of title page. / Document formatted into pages; contains 84 pages. / Thesis (M.S.C.S.)--University of South Florida, 2003. / Includes bibliographical references. / Text (Electronic thesis) in PDF format. / ABSTRACT: New generations of video surveillance systems are expected to possess a large-scale network of intelligent video cameras with built-in image processing capabilities. These systems need to be tethered for reasons of bandwidth and power requirements. To support economical installation of video cameras and to manage the huge volume of information flow in these networks, there is a need for new shared-medium daisy-chained physical and medium access control (bus arbitration) layer communication protocols. This thesis describes the design principles of Spatial reuse FireWire Protocol (SFP), a novel request/grant bus arbitration protocol, architected for an acyclic daisy-chained network topology. SFP is a new extension of the IEEE 1394b FireWire architecture. / ABSTRACT: SFP preserves the simple repeat path functionality of FireWire while offering two significant advantages: 1) SFP supports concurrent data transmissions over disjoint segments of the network (spatial reuse of bandwidth), which increases the effective throughput and 2) SFP provides support for priority traffic, which is necessary to handle real-time applications (like packet video), and mission critical applications (like event notifications between cameras) that have strict delay and jitter constraints. The delay and throughput performance of FireWire and SFP were evaluated using discrete-event queuing simulation models built with the CSIM-18 simulation library. Simulation results show that for a homogeneous traffic pattern SFP improves upon the throughput of IEEE 1394b by a factor of 2. For a traffic pattern typical of video surveillance applications, throughput increases by a factor of 7. / ABSTRACT: Simulation results demonstrate that IEEE 1394b asynchronous stream based packet transactions offer better delay performance than isochronous transactions for variable bit rate video like MPEG-2 and MPEG-4. SFP extends this observation by supporting priority traffic. QoS for packet video is provided in SFP by mapping individual asynchronous stream packets to the three priority classes. / System requirements: World Wide Web browser and PDF reader. / Mode of access: World Wide Web.