Double Difference Motion Detection and Its Application for Madura Batik Virtual Fitting Room

Madura Batik Virtual Fitting Room using double difference algorithms motion detection is proposed in this research. This virtual fitting room consists of three main stages, i.e. motion detection, determination of region of interest of the detected motion, superimposed the virtual clothes into the region of interest. The double difference algorithm is used for the motion detection stage, since in this algorithm, the empty frame as the reference frame is not required. The double difference algorithm uses the previous and next frame to detect the motion in the current frame. Perception Test Images Sequences Dataset are used as the data of the experiment to measure the performance accuracy of this algorithm before the algorithm is used for the Madura batik virtual fitting room. The accuracy is 57.31%, 99.71%, and 78.52% for the sensitivity, specificity, and balanced accuracy, respectively. The build Madura batik virtual fitting room in this research can be used as the added feature of the Madura batik online stores, hence the consumer is able to see whether the clothes is fitted to them or not, and this virtual fitting room is also can be used as the promotion of Madura batik broadly

Improved motion segmentation based on shadow detection

In this paper, we discuss common colour models for background subtraction and problems related to their utilisation are discussed. A novel approach to represent chrominance information more suitable for robust background modelling and shadow suppression is proposed. Our method relies on the ability to represent colours in terms of a 3D-polar coordinate system having saturation independent of the brightness function; specifically, we build upon an Improved Hue, Luminance, and Saturation space (IHLS). The additional peculiarity of the approach is that we deal with the problem of unstable hue values at low saturation by modelling the hue-saturation relationship using saturation-weighted hue statistics. The effectiveness of the proposed method is shown in an experimental comparison with approaches based on RGB, Normalised RGB and HSV

Parametric tracking with spatial extraction across an array of cameras

Video surveillance is a rapidly growing area that has been fuelled by an increase in the concerns of security and safety in both public and private areas. With heighten security concerns, the utilization of video surveillance systems spread over a large area is becoming the norm. Surveillance of a large area requires a number of cameras to be deployed, which presents problems for human operators. In the surveillance of a large area, the need to monitor numerous screens makes an operator less effective in monitoring, observing or tracking groups or targets of interest. In such situations, the application of computer systems can prove highly effective in assisting human operators. The overall aim of this thesis was to investigate different methods for tracking a target across an array of cameras. This required a set of parameters to be identified that could be passed between cameras as the target moved in and out of the fields of view. Initial investigations focussed on identifying the most effective colour space to use. A normalized cross correlation method was used initially with a reference image to track the target of interest. A second method investigated the use of histogram similarity in tracking targets. In this instance a reference target’s histogram or pixel distribution was used as a means for tracking. Finally a method was investigated that used the relationship between colour regions that make up a whole target. An experimental method was developed that used the information between colour regions such as the vector and colour difference as a means for tracking a target. This method was tested on a single camera configuration and multiple camera configuration and shown to be effective. In addition to the experimental tracking method investigated, additional data can be extracted to estimate a spatial map of a target as the target of interest is tracked across an array of cameras. For each method investigated the experimental results are presented in this thesis and it has been demonstrated that minimal data exchange can be used in order to track a target across an array of cameras. In addition to tracking a target, the spatial position of the target of interest could be estimated as it moves across the array

Previsão e identificação de eventos de quebra de segurança em vídeo-vigilância

Tese de doutoramento em Tecnologias e Sistemas de Informação (ramo de conhecimento em Sistemas de Computação e Comunicação)Esta tese tem como propósito a detecção e previsão de comportamentos passíveis de originar uma quebra de segurança. Estes são reconhecidos por meio da observação de padrões de actividade humana, extraídos de sequências de imagens digitalizadas, adquiridas por intermédio de uma câmara de vídeo a cores, monocular e fixa. A aferição dos comportamentos é suportada pela informação obtida através da detecção, classificação e seguimento de objectos em movimento, minimizando a utilização de informação de contexto na cena observada e sem recurso a descrições de comportamentos previamente definidos. De modo a atingir este objectivo, foram desenvolvidas técnicas de processamento e análise de imagem, associadas a métodos baseados em inteligência artificial para a modelação de padrões de comportamento. A segmentação de objectos em movimento foi assente numa abordagem de subtracção por plano de fundo adaptativo, com a capacidade de detecção de regiões da imagem afectadas por sombras e brilhos. Criou-se ainda um processo de remoção de fantasmas, i.e. falsas detecções observadas sempre que um objecto, pertencente ao plano de fundo, inicia um movimento de deslocação que o leva a abandonar o espaço anteriormente ocupado. O seguimento de objectos foi assegurado por uma técnica que recorre a Modelos de Aparência, e que possibilita o seguimento de objectos deformáveis, mostrando-se eficaz em situações de oclusão, fusão e separação de objectos. Para a detecção e previsão automática de comportamentos desenvolveram-se dois classificadores (N-ary Trees e Dynamic Oriented Graph) que, utilizando os dados provenientes das funções de processamento e análise de imagem, permitem modelar sequências temporais. O sistema final, constituído pela junção dos múltiplos componentes propostos e implementado numa câmara de vídeo inteligente, foi testado com um conjunto de dados sintéticos, sendo posteriormente avaliado em ambiente real de vídeo-vigilância. Pela análise dos resultados experimentais, verificou-se que o sistema proposto permite realizar de forma eficaz a previsão de comportamentos de quebra de segurança.This thesis has the purpose of detection and forecasting of behaviours susceptible to originate security breaks. These behaviours are recognized by means of human activity pattern observation, extracted from digital image sequences, acquired by a video colour camera, monocular and static. The assessment of the behaviours is supported by the information acquired through the detection, classification and tracking of moving objects, when minimizing the use of context information from the observed scene and without descriptions of previously defined behaviours. In order to reach this goal, image processing and analysis techniques had been developed and associated with artificial intelligence methods for the behaviour pattern modelling. The segmentation of moving objects was based on an adaptive background subtraction approach capable of detecting regions of the image affected by shadows and highlights. A ghost’s removal process was also developed, i.e. observed false detections whenever one object, pertaining to the background, initiates a movement that takes it to abandon the previously occupied space. The tracking of objects was assured by a technique that applies Appearance Models, which makes possible the tracking of deformable objects and reveals efficiency in situations of occlusion, fusion and splitting of objects. For the detection and automatic forecasting of behaviours, two classifiers (N-ary Trees and Dynamic Oriented Graph) were proposed. Both use preceding data from the processing and image analysis functions and they allow the modelling of temporal sequences. The overall system, built from the junction of the components developed and implemented in an intelligent video camera, was tested with a synthetic dataset, being later evaluated in real environment of video-monitoring. The analysis of the experimental results has shown that the proposed system allows an efficient prediction of security break behaviours.Fundação para a Ciência e a Tecnologia (FCT) - referência SFRH / BD / 17259 / 200