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Security cameras installed in public spaces or in private organizations continuously record video data with the aim of detecting and preventing crime. For that reason, video content analysis applications, either for real time (i.e. analytic) or post-event (i.e. forensic) analysis, have gained high interest in recent years. In this thesis, the primary focus is on two key aspects of video analysis, reliable moving object segmentation and carried object detection & identification. A novel moving object segmentation scheme by background subtraction is presented in this thesis. The scheme relies on background modelling which is based on multi-directional gradient and phase congruency. As a post processing step, the detected foreground contours are refined by classifying the edge segments as either belonging to the foreground or background. Further contour completion technique by anisotropic diffusion is first introduced in this area. The proposed method targets cast shadow removal, gradual illumination change invariance, and closed contour extraction. A state of the art carried object detection method is employed as a benchmark algorithm. This method includes silhouette analysis by comparing human temporal templates with unencumbered human models. The implementation aspects of the algorithm are improved by automatically estimating the viewing direction of the pedestrian and are extended by a carried luggage identification module. As the temporal template is a frequency template and the information that it provides is not sufficient, a colour temporal template is introduced. The standard steps followed by the state of the art algorithm are approached from a different extended (by colour information) perspective, resulting in more accurate carried object segmentation. The experiments conducted in this research show that the proposed closed foreground segmentation technique attains all the aforementioned goals. The incremental improvements applied to the state of the art carried object detection algorithm revealed the full potential of the scheme. The experiments demonstrate the ability of the proposed carried object detection algorithm to supersede the state of the art method.

People counting system involves the process of counting and estimating the number of people in a scene. The counting system has a number of useful applications, ranging from pedestrian traffic surveillance and monitoring the number of people that enters and leaves shopping malls to commercial buildings, vehicles and a number of other security-related applications. Over the years, significant progress has been made. However, people counting systems still have not overcome a number of challenges such as occlusions, human pose and direction, multiple people detection, varying lighting and weather conditions. The aim of this research is to present an optimal solution that is invariant to the challenges. That is, the outcome of the results will not be affected by the challenges. Also, the solution will handle the trade-off between the counting accuracy and the time it takes to implement the counting process. As a result, a new background subtraction method known as single pixel method is proposed. This is where useful features are collected from each scene using frame difference method. Then, these features are reduced into single pixels. The single pixels are then used to estimate the total number of people in the scene. Furthermore, a virtual-line direction-estimation method is presented where the directions in which the people are heading are estimated prior to counting. AFRIKAANS : Mense-telstelsels behels die proses van die tel en die beraming van die aantal mense op ’n toneel. Die telstelsel het ’n aantal nuttige toepassings wat wissel van voetgangerverkeer toesig en die monitering van die aantal mense wat binnekom en verlaat tot winkelsentrums, kommersiële geboue, voertuie, en ’n aantal ander sekuriteit-verwante programme. Oor die jare is beduidende vordering gemaak. Daar is egter ’n aantal uitdagings wat mense-telstelsels nog nie oorkom het nie, soos afsluiting, menslike inhou en rigting, die opsporing van veelvoudige mense, wisselende beligting en weerstoestande. Die doel van hierdie navorsing is om ’n optimale oplossing aan te bied, wat invariant is teen die uitdagings. Met ander woorde, die uitdagings sal nie die resultate affekteer nie. Die oplossing sal ook die uitruil tussen die tel akkuraatheid en die implementeringstyd van die telproses hanteer. As gevolg hiervan, is ’n nuwe agtergrondaftrekkingsmetode, wat bekend staan as ’n enkele beeldelement metode, voorgestel. Dit is waar die nuttige funksies van elke toneel, met behulp van die raamverskilmetode ingesamel word. Dan word hierdie eienskappe in enkele beeldelemente verminder. Die enkele beeldelemente word dan gebruik om die totale aantal mense in die toneel te skat. Verder is daar van ’n virtuele-lyn rigting-skatting metode gebruik gemaak wat die rigtings waarin die mense beweeg vooraf beraam. / Dissertation (MEng)--University of Pretoria, 2013. / Electrical, Electronic and Computer Engineering / unrestricted