Thesis (M.S.)--Kutztown University of Pennsylvania, 1994. / Source: Masters Abstracts International, Volume: 45-06, page: 2806. Abstract precedes thesis as [1] preliminary leaf. Typescript. Includes bibliographical references (leaves 52-53).

Thesis (M.Phil) - University of Queensland, 2004. / Includes bibliography.

Thesis (M.E.)--University of Canterbury, 2008. / Typescript (photocopy). "September 2008." Includes bibliographical references (p. 157-163). Also available via the World Wide Web.

Originally presented as the author's Thesis--Zugl.: Technische Universität München, 2007. / Includes bibliographical references.

With the growing installation of surveillance video cameras in both private and public areas, it is an immediate requirement to develop intelligent video analysis system for the large-scale camera network. As a prerequisite step of person tracking and person retrieval in intelligent video analysis, person re-identification, which targets in matching person images across camera views is an important topic in computer vision community and has been received increasing attention in the recent years. In the supervised learning methods, the person re-identification task is formulated as a classification problem to extract matched person images/videos (positives) from unmatched person images/videos (negatives). Although the state-of-the-art supervised classification models could achieve encouraging re-identification performance, the assumption that label information is available for all the cameras, is impractical in large-scale camera network. That is because collecting the label information of every training subject from every camera in the large-scale network can be extremely time-consuming and expensive. While the unsupervised learning methods are flexible, their performance is typically weaker than the supervised ones. Though sufficient labels of the training subjects are not available from all the camera views, it is still reasonable to collect sufficient labels from a pair of camera views in the camera network or a few labeled data from each camera pair. Along this direction, we address two scenarios of person re-identification in large-scale camera network in this thesis, i.e. unsupervised domain adaptation and semi-supervised learning and proposed three methods to learn discriminative model using all available label information and domain knowledge in person re-identification. In the unsupervised domain adaptation scenario, we consider data with sufficient labels as the source domain, while data from the camera pair missing label information as the target domain. A novel domain adaptive approach is proposed to estimate the target label information and incorporate the labeled data from source domain with the estimated target label information for discriminative learning. Since the discriminative constraint of Support Vector Machines (SVM) can be relaxed into a necessary condition, which only relies on the mean of positive pairs (positive mean), a suboptimal classification model learning without target positive data can be those using target positive mean. A reliable positive mean estimation is given by using both the labeled data from the source domain and potential positive data selected from the unlabeled data in the target domain. An Adaptive Ranking Support Vector Machines (AdaRSVM) method is also proposed to improve the discriminability of the suboptimal mean based SVM model using source labeled data. Experimental results demonstrate the effectiveness of the proposed method. Different from the AdaRSVM method that using source labeled data, we can also improve the above mean based method by adapting it onto target unlabeled data. In more general situation, we improve a pre-learned classifier by adapting it onto target unlabeled data, where the pre-learned classifier can be domain adaptive or learned from only source labeled data. Since it is difficult to estimate positives from the imbalanced target unlabeled data, we propose to alternatively estimate positive neighbors which refer to data close to any true target positive. An optimization problem for positive neighbor estimation from unlabeled data is derived and solved by aligning the cross-person score distributions together with optimizing for multiple graphs based label propagation. To utilize the positive neighbors to learn discriminative classification model, a reliable multiple region metric learning method is proposed to learn a target adaptive metric using regularized affine hulls of positive neighbors as positive regions. Experimental results demonstrate the effectiveness of the proposed method. In the semi-supervised learning scenario, we propose a discriminative feature learning using all available information from the surveillance videos. To enrich the labeled data from target camera pair, image sequences (videos) of the tagged persons are collected from the surveillance videos by human tracking. To extract the discriminative and adaptable video feature representation, we propose to model the intra-view variations by a video variation dictionary and a video level adaptable feature by multiple sources domain adaptation and an adaptability-discriminability fusion. First, a novel video variation dictionary learning is proposed to model the large intra-view variations and solved as a constrained sparse dictionary learning problem. Second, a frame level adaptable feature is generated by multiple sources domain adaptation using the variation modeling. By mining the discriminative information of the frames from the reconstruction error of the variation dictionary, an adaptability-discriminability (AD) fusion is proposed to generate the video level adaptable feature. Experimental results demonstrate the effectiveness of the proposed method.

Orientador: Laymert Garcia dos Santos / Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Filosofia e Ciencias Humanas / Made available in DSpace on 2018-08-06T03:18:07Z (GMT). No. of bitstreams: 1 Kanashiro_MartaMourao_M.pdf: 1988987 bytes, checksum: b6b82fb95441f45b67f0253e0d7f91e9 (MD5) Previous issue date: 2006 / Resumo: Tendo em vista a proliferação de mecanismos de vigilância e controle nas sociedades contemporâneas ocidentais, esta dissertação busca refletir sobre as representações e discursos associados à inserção das câmeras de monitoramento para segurança no cotidiano brasileiro. A partir de um estudo de caso realizado na região central da cidade de São Paulo (Parque da Luz), do levantamento das proposições e normas legais que versam sobre o tema e do acompanhamento de publicações e feiras do setor de segurança eletrônica, procura-se fazer emergir tais discursos. A transformação da segurança em mercadoria e sua promoção por meio da idéia de prevenção ou antecipação são alguns dos aspectos percebidos na pesquisa como profundamente equacionados com essa prática. Baseando-se em aspectos como esses, argumenta-se que as câmeras de monitoramento participam de uma forma de exercício do poder na atualidade, que focaliza fluxos e mobilidade em detrimento do ¿indivíduo¿ / Abstract: Considering the proliferation of surveillance and control mechanisms in the occidental contemporaneous societies, this dissertation aims at reflecting on the representations and discourses associated with the input of security camera monitoring system (CCTVs) in the Brazilian daily routine. Starting from a case study in the downtown area in São Paulo City (Parque da Luz), a survey of propositions and legal norms which deal with the subject, and going over publications and electronic security sector fairs, it aims at emerging these different discourses. The transformation of security into goods and its promotion by the idea of prevention or anticipation are some of the aspects focused on this research as deeply equated with this practice. Based on aspects like those, it is argued that the monitoring cameras are part of the current power practice, which focused on flows and mobility to the detriment of the "individual" / Mestrado / Mestre em Sociologia