Foreground detection of video through the integration of novel multiple detection algorithims

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel UniversityThe main outcomes of this research are the design of a foreground detection algorithm, which is more accurate and less time consuming than existing algorithms. By the term accuracy we mean an exact mask (which satisfies the respective ground truth value) of the foreground object(s). Motion detection being the prior component of foreground detection process can be achieved via pixel based and block based methods, both of which have their own merits and disadvantages. Pixel based methods are efficient in terms of accuracy but a time consuming process, so cannot be recommended for real time applications. On the other hand block based motion estimation has relatively less accuracy but consumes less time and is thus ideal for real-time applications. In the first proposed algorithm, block based motion estimation technique is opted for timely execution. To overcome the issue of accuracy another morphological based technique was adopted called opening-and-closing by reconstruction, which is a pixel based operation so produces higher accuracy and requires lesser time in execution. Morphological operation opening-and-closing by reconstruction finds the maxima and minima inside the foreground object(s). Thus this novel simultaneous process compensates for the lower accuracy of block based motion estimation. To verify the efficiency of this algorithm a complex video consisting of multiple colours, and fast and slow motions at various places was selected. Based on 11 different performance measures the proposed algorithm achieved an average accuracy of more than 24.73% than four of the well-established algorithms. Background subtraction, being the most cited algorithm for foreground detection, encounters the major problem of proper threshold value at run time. For effective value of the threshold at run time in background subtraction algorithm, the primary component of the foreground detection process, motion is used, in this next proposed algorithm. For the said purpose the smooth histogram peaks and valley of the motion were analyzed, which reflects the high and slow motion areas of the moving object(s) in the given frame and generates the threshold value at run time by exploiting the values of peaks and valley. This proposed algorithm was tested using four recommended video sequences including indoor and outdoor shoots, and were compared with five high ranked algorithms. Based on the values of standard performance measures, the proposed algorithm achieved an average of more than 12.30% higher accuracy results

Biologically inspired, self organizing communication networks.

PhDThe problem of energy-efficient, reliable, accurate and self-organized target tracking in Wireless Sensor Networks (WSNs) is considered for sensor nodes with limited physical resources and abrupt manoeuvring mobile targets. A biologically inspired, adaptive multi-sensor scheme is proposed for collaborative Single Target Tracking (STT) and Multi-Target Tracking (MTT). Behavioural data obtained while tracking the targets including the targets’ previous locations is recorded as metadata to compute the target sampling interval, target importance and local monitoring interval so that tracking continuity and energy-efficiency are improved. The subsequent sensor groups that track the targets are selected proactively according to the information associated with the predicted target location probability such that the overall tracking performance is optimized or nearly-optimized. One sensor node from each of the selected groups is elected as a main node for management operations so that energy efficiency and load balancing are improved. A decision algorithm is proposed to allow the “conflict” nodes that are located in the sensing areas of more than one target at the same time to decide their preferred target according to the target importance and the distance to the target. A tracking recovery mechanism is developed to provide the tracking reliability in the event of target loss. The problem of task mapping and scheduling in WSNs is also considered. A Biological Independent Task Allocation (BITA) algorithm and a Biological Task Mapping and Scheduling (BTMS) algorithm are developed to execute an application using a group of sensor nodes. BITA, BTMS and the functional specialization of the sensor groups in target tracking are all inspired from biological behaviours of differentiation in zygote formation. Simulation results show that compared with other well-known schemes, the proposed tracking, task mapping and scheduling schemes can provide a significant improvement in energy-efficiency and computational time, whilst maintaining acceptable accuracy and seamless tracking, even with abrupt manoeuvring targets.Queen Mary university of London full Scholarshi

Filtragem Não Linear Adaptativa e Seguimento Radar Ótimo de Veículos Aeroespaciais

A filtragem não-linear é um dos tópicos mais importantes e complexos em engenharia, especialmente quando aplicada a situações de tempo-real em ambientes altamente não-lineares. Este é o cenário da maioria das aplicações aeroespaciais nomeadamente, aviso de colisão, seguimento radar, vigilância, orientação, navegação e controlo de veículos aeroespaciais, sendo que o principal objetivo é a estimação dos estados de um determinado alvo (seja este uma aeronave, satélite, míssil ou outro) a partir de medições ruidosas. A maior dificuldade está em desenvolver métodos que sejam capazes de lidar não só com a não-linearidade dos modelos, mas também com as incertezas associadas aos instrumentos de medições e às perturbações existentes no meio envolvente que afetam diretamente o sistema e, na sua maioria, são difíceis de prever e computar. Uma das estratégias mais utilizadas para garantir o ajuste dinâmico e ótimo dos métodos de filtragem face a todas estas adversidades é a implementação de algoritmos adaptativos. Assim sendo, a abordagem mais utilizada para lidar com esta problemática é a filtragem de Kalman. O seu sucesso, principalmente na área de engenharia, deve-se na sua maioria ao filtro de Kalman estendido (EKF – Extended Kalman Filter). Este assenta no pressuposto de que a linearização é suficiente para representar localmente a não-linearidade do sistema e, por conseguinte, o algoritmo utiliza o modelo linearizad0 em substituição ao modelo original não-linear. A linearização é um processo relativamente fácil de compreender e aplicar, o que justifica a popularidade do filtro. Contudo, ao lidar com sistemas altamente não-lineares, o EKF tende a apresentar algumas limitações, tais como, estimativas erráticas, comportamentos instáveis e por vezes até divergentes. De forma a colmatar algumas destas limitações, esta tese apresenta um filtro de Kalman estendido melhorado e adaptativo, denominado por improved Extended Kalman Filter (iEKF), onde para além da adaptabilidade clássica das matrizes de ruído, é proposto uso da norma de Frobenius como fator de correção da estimativa da covariância a priori e é também proposto um novo ponto de linearização. Desta forma, o iEKF adapta as matrizes de transição dos modelos através do novo ponto de linearização e adapta as informações estatísticas através da matriz de covariância proposta. A principal intenção é manter a simplicidade e estrutura pelo qual o EKF é conhecido, porém melhorar o seu desempenho e precisão com conceitos simples, eficazes e adaptativos. Um outro foco desta tese é analisar o desempenho da filtragem no seguimento radar. Assim sendo, tanto o EKF como o iEKF foram implementados e analisados em quatro aplicações deste âmbito, sendo estas: a estimação de uma órbita de um satélite artificial, a estimação de uma transferência orbital (transferência de Hohmann), a estimação de uma reentrada na atmosfera, e por fim, a estimação da trajetória de uma aeronave comercial, em que objetivo é estimar a posição e velocidade do veículo. Tanto o EKF como o iEKF foram analisados e comparados com base no RMSE (Root Mean Square Error). Os resultados demonstram que o iEKF fornece estimativas superiores. O algoritmo é, em geral, mais preciso, estável e confiável, demonstrando ser uma alternativa conveniente ao clássico EKF. Em suma, esta tese propõe um novo método de filtragem não-linear adaptativo, denominado por iEKF. Os resultados indicam que este deve ser tido em consideração para a estimação de estados não-linear tanto para o seguimento radar, como para qualquer outra área que necessidade de um algoritmo de filtragem eficiente.Nonlinear filtering is an important and complex topic in engineering, especially when applied to real-time applications with a highly nonlinear environment. This scenario involves most aerospace applications, such as surveillance, guidance, navigation, attitude control, collision warning and target tracking, where the main objective consists of estimating the states of a moving target (aircraft, satellite, missile, spacecraft, etc.) based on noisy measurements. The challenge is to develop methods that are capable to cope, not only with the nonlinearities of the models but also with the instrumental inaccuracies related to the data acquisition system and the environmental perturbations that are unwanted and, in most cases, difficult to compute. One of the promising strategies to dynamically adjust and guarantee filter optimality is the computation of adaptative algorithms. A very well-known framework to deal with those problems is the Kalman filter algorithms, whose success in engineering applications is mostly due to the Extended Kalman Filter (EKF). The EKF is based on the assumption that a local linearization of the system may be a sufficient description of nonlinearities, therefore the linearized model is used instead of the original nonlinear function. Such approximations are easy to understand and apply, which explains the popularity of the filter. However, when dealing with highly nonlinear systems, the EKF estimates suffer serious problems, such as unstable and quickly divergent behaviours and/or erratic estimates. To address those limitations, this thesis proposes an improved Extended Kalman filter (iEKF) with an adaptative structure, where a new Jacobian matrix expansion point is proposed, and a Frobenius norm of the covariance matrix is suggested as a correction factor for the a priori estimates. Therefore, the iEKF does not only update the statistical information based on the proposed covariance matrix but also updates the state and measurements transitions matrices based on the new Jacobian expansion point. The core idea is to maintain the EKF structure and simplicity but improve the overall performance with simple yet effective concepts. Another objective of this thesis was to evaluate the performance of the filtering methods on radar tracking applications. Thus, the effectiveness of EKF and iEKF were analysed and compared in four radar tracking applications: an artificial satellite orbit estimation, a Hohmann orbit transfer, an atmospheric reentry estimation, and a commercial aircraft trajectory estimation, where the position and velocity of the aerospace vehicle were computed. The EKF and iEKF were compared based on the RMSE (Root Mean Square Error). Simulations results suggest that the iEKF provides a considerably higher accuracy on the overall results. The algorithm is more precise, stable, and reliable, which make it an attractive alternative to the classic EKF. In summary, this thesis proposed an improved Extended Kalman Filter with an adaptative structure. This algorithm is a promising method for nonlinear state estimation, not only for radar tracking applications but any applications that require an efficient nonlinear filter

A Proactive Approach to Application Performance Analysis, Forecast and Fine-Tuning

A major challenge currently faced by the IT industry is the cost, time and resource associated with repetitive performance testing when existing applications undergo evolution. IT organizations are under pressure to reduce the cost of testing, especially given its high percentage of the overall costs of application portfolio management. Previously, to analyse application performance, researchers have proposed techniques requiring complex performance models, non-standard modelling formalisms, use of process algebras or complex mathematical analysis. In Continuous Performance Management (CPM), automated load testing is invoked during the Continuous Integration (CI) process after a build. CPM is reactive and raises alarms when performance metrics are violated. The CI process is repeated until performance is acceptable. Previous and current work is yet to address the need of an approach to allow software developers proactively target a specified performance level while modifying existing applications instead of reacting to the performance test results after code modification and build. There is thus a strong need for an approach which does not require repetitive performance testing, resource intensive application profilers, complex software performance models or additional quality assurance experts. We propose to fill this gap with an innovative relational model associating the operation‟s Performance with two novel concepts – the operation‟s Admittance and Load Potential. To address changes to a single type or multiple types of processing activities of an application operation, we present two bi-directional methods, both of which in turn use the relational model. From annotations of Delay Points within the code, the methods allow software developers to either fine-tune the operation‟s algorithm “targeting” a specified performance level in a bottom-up way or to predict the operation‟s performance due to code changes in a top-down way under a given workload. The methods do not need complex performance models or expensive performance testing of the whole application. We validate our model on a realistic experimentation framework. Our results indicate that it is possible to characterize an application Performance as a function of its Admittance and Load Potential and that the application Admittance can be characterized as a function of the latency of its Delay Points. Applying this method to complex large-scale systems has the potential to significantly reduce the cost of performance testing during system maintenance and evolution