Thesis (MCIS)--AUT University, 2009. / Includes bibliographical references. Also held in print ([12], 94 leaves : ill. ; 30 cm.) in the Archive at the City Campus (T 621.384191 BAI)

Thesis (M.S.)--Missouri State University, 2009. / "May 2009." Includes bibliographical references (leaves 103-109). Also available online.

Thesis (M.S.)--Rochester Institute of Technology, 2006. / Typescript. Includes bibliographical references (leaves 76-78).

Thesis (Ph. D.)--University of Texas at Austin, 2004. / Supervisor: J.K. Aggarwal. Vita. Includes bibliographical references.

Thesis (Ph. D.)--Lehigh University, 2006. / Includes vita. Includes bibliographical references (leaves 114-124).

Thesis (Ph. D.)--State University of New York at Binghamton, Thomas J. Watson School of Engineering and Applied Science, Department of Electrical Engineering, 2008. / Includes bibliographical references.

[pt] Sistemas computadorizados de vigilância de veículos têm despertado grande interesse devido à demanda para automatizar tarefas que atualmente são realizadas por operadores humanos. Porém, para realizar estas tarefas é preciso resolver alguns problemas clássicos de visão computacional como sombras, oclusões e variação de iluminação. Este trabalho propõe algoritmos em tempo real para máquinas de baixo custo com o objetivo de rastrear, classificar e determinar a velocidade de cada veículo de uma rodovia. / [en] Vehicle surveillance computerized systems have grown great interest due to the automatizing duties demand, which recently executed by computer vision like shadows, occlusion and light variation have to be solved. The present work proposes real time algorithms for low cost machines focused on tracking, classifying and determining each vehicle`s speed on a highway.

M.Ing. / There is a growing need to enhance situation awareness in the maritime environment utilizing new and current technologies. There are numerous ways to enhance situation awareness by employing long-range vision detection systems, data fusion techniques, such as combining radar and automatic identification system (AIS) data and data mining techniques that allow for filtering out anomalies. With the proliferation of high-quality video equipment and cheaper and faster computational machines, there is an increasing need for automated video surveillance as the amount of information available to the operator for processing is overwhelming. It is therefore necessary that only crucial information that may negatively impact mission effectiveness be presented to the operator. Whilst performing surveillance one would be interested in monitoring other surface vessels within the sensor coverage. The detection and tracking of small and slow moving targets having low signal-to-noise ratios is of interest in the maritime environment. This is particularly challenging as influences from the natural environment, such as sea states, glint, whitecaps and clutter, on a target is captured during image acquisition and this has adverse effects on the tracking of a target. A grey-scale based target tracking algorithm using the particle filter framework was developed and tested in MATLAB® (R2008a). The main focus of the work is on the use of dynamic models in a particle filtering framework. The dynamic model contributes to the propagation of the particles in a particle filtering framework of the target grey-scale distribution. The dynamic models investigated are the constant velocity model and an acceleration model. The algorithm was tested with real-world image sequences in the maritime environment. The targets were tracked for the duration of the image sequence and the dynamic model that accounted for acceleration yielded better results when analysing the position error between the estimated position and the ground truth data points. A slight improvement in this error makes a significant difference on tracking a target as targets in the maritime environment context are small. The future scope of the work would then include accounting for more features of the target such as edge cues and/or implementing adaptive observation models to improve the accuracy, stability and robustness of the algorithm for real-time applications.

The increasingly widespread use of surveillance as a cornerstone of crime control presents novel challenges to questions about personal autonomy, stigmatization, and the shape of social processes. In New York City, the end of stop-and-frisk policing meant the rise of “omnipresence,” built on a progression of the surveillance infrastructure. For residents of NYCHA public housing developments in New York City, the installation of highly visible surveillance structures provokes questions about the role of surveillance in increasing contact with the criminal justice system, as well about how use of these structures redefines spaces against a background of gentrification, a globalizing real estate market, and unbuffered income stratification. This project uses ethnographic methods, including interviews and participant observation to explore the phenomenology of social processes undertaken by individuals in relation to surveillance structures and to interrogate the use of surveillance in public housing. Engaging with the work of Bourdieu, Lefebvre, Wacquant, Goffman, and Sassen, the dissertation explores social ordering, stigmatization, norm durability, and place-making among NYCHA residents. The experiences of residents of neighborhoods that are on both sides of the city-wide demographic shifts associated with gentrification are further contrasted, contextualizing their interactions at the macro and micro levels. At the policy level, NYCHA housing becomes a focus of crime control measures as if containing public housing will address the root causes of crime. On the ground, however, cameras do not work to prevent crime, even when they are in good repair. Lights may make some individuals feel safer walking through courtyards late at night, but those same individuals fear that danger is just beyond the reach of the lights in the stairwells or playgrounds. If the cameras and lights fail to allay the fears of those in public housing, if their experiences with the system of surveillance have proven unsuccessful in preventing crime, giving the feeling of safety, or helping to solve crime, what is their purpose? This study posits that these structures are a spectacle. They are structures heavily laden with symbolism that reassure non-residents and residents alike of the neutralization of NYCHA residents. This symbolic dynamic ultimately stigmatizes NYCHA residents and pushes them further towards the systemic edge. Among the theoretical implications of the dissertation’s conclusions are an enhanced understanding of the connections between the persistence of social inequality, the “terra non grata” of certain urban spaces, and the dismantling of the social welfare state. The practical implications of this work are significant and add to discourses around the function of technology in the creation of new types of barriers.

Thesis (MSc)--Stellenbosch University, 2014. / ENGLISH ABSTRACT: Object detection and tracking are important components of many computer vision applications including automated surveillance. Automated surveillance attempts to solve the challenges associated with closed-circuit camera systems. These include monitoring large numbers of cameras and the associated labour costs, and issues related to targeted surveillance. Object detection is an important step of a surveillance system and must overcome challenges such as changes in object appearance and illumination, dynamic background objects like ickering screens, and shadows. Our system uses Gaussian mixture models, which is a background subtraction method, to detect moving objects. Tracking is challenging because measurements from the object detection stage are not labelled and could be from false targets. We use multiple hypothesis tracking to solve this measurement origin problem. Practical long-term tracking of objects should have re-identi cation capabilities to deal with challenges arising from tracking failure and occlusions. In our system each tracked object is assigned a one-class support vector machine (OCSVM) which learns the appearance of that object. The OCSVM is trained online using HSV colour features. Therefore, objects that were occluded or left the scene can be reidenti ed and their tracks extended. Standard, publicly available data sets are used for testing. The performance of the system is measured against ground truth using the Jaccard similarity index, the track length and the normalized mean square error. We nd that the system performs well. / AFRIKAANSE OPSOMMING: Die opsporing en volging van voorwerpe is belangrike komponente van baie rekenaarvisie toepassings, insluitend outomatiese bewaking. Outomatiese bewaking poog om die uitdagings wat verband hou met geslote kring kamera stelsels op te los. Dit sluit in die monitering van groot hoeveelhede kameras en die gepaardgaande arbeidskoste, en kwessies wat verband hou met toegespitse bewaking. Die opsporing van voorwerpe is 'n belangrike stap in 'n bewakingstelsel en moet uitdagings soos veranderinge in die voorwerp se voorkoms en beligting, dinamiese agtergrondvoorwerpe soos ikkerende skerms, en skaduwees oorkom. Ons stelsel maak gebruik van Gaussiese mengselmodelle, wat 'n agtergrond-aftrek metode is, om bewegende voorwerpe op te spoor. Volging is 'n uitdaging, want afmetings van die voorwerp-opsporing stadium is nie gemerk nie en kan afkomstig wees van valse teikens. Ons gebruik verskeie hipotese volging (multiple hypothesis tracking ) om hierdie meting-oorsprong probleem op te los. Praktiese langtermynvolging van voorwerpe moet heridenti seringsvermoëns besit, om die uitdagings wat voortspruit uit mislukte volging en okklusies te kan hanteer. In ons stelsel word elke gevolgde voorwerp 'n een-klas ondersteuningsvektormasjien (one-class support vector machine, OCSVM) toegeken, wat die voorkoms van daardie voorwerp leer. Die OCSVM word aanlyn afgerig met die gebruik van HSV kleurkenmerke. Daarom kan voorwerpe wat verdwyn later her-identi seer word en hul spore kan verleng word. Standaard, openbaar-beskikbare datastelle word vir toetse gebruik. Die prestasie van die stelsel word gemeet teen korrekte afvoer, met behulp van die Jaccard ooreenkoms-indeks, die spoorlengte en die genormaliseerde gemiddelde kwadraatfout. Ons vind dat die stelsel goed presteer.