Random finite sets in multi-target tracking - efficient sequential MCMC implementation

Over the last few decades multi-target tracking (MTT) has proved to be a challenging and attractive research topic. MTT applications span a wide variety of disciplines, including robotics, radar/sonar surveillance, computer vision and biomedical research. The primary focus of this dissertation is to develop an effective and efficient multi-target tracking algorithm dealing with an unknown and time-varying number of targets. The emerging and promising Random Finite Set (RFS) framework provides a rigorous foundation for optimal Bayes multi-target tracking. In contrast to traditional approaches, the collection of individual targets is treated as a set-valued state. The intent of this dissertation is two-fold; first to assert that the RFS framework not only is a natural, elegant and rigorous foundation, but also leads to practical, efficient and reliable algorithms for Bayesian multi-target tracking, and second to provide several novel RFS based tracking algorithms suitable for the specific Track-Before-Detect (TBD) surveillance application. One main contribution of this dissertation is a rigorous derivation and practical implementation of a novel algorithm well suited to deal with multi-target tracking problems for a given cardinality. The proposed Interacting Population-based MCMC-PF algorithm makes use of several Metropolis-Hastings samplers running in parallel, which interact through genetic variation. Another key contribution concerns the design and implementation of two novel algorithms to handle a varying number of targets. The first approach exploits Reversible Jumps. The second approach is built upon the concepts of labeled RFSs and multiple cardinality hypotheses. The performance of the proposed algorithms is also demonstrated in practical scenarios, and shown to significantly outperform conventional multi-target PF in terms of track accuracy and consistency. The final contribution seeks to exploit external information to increase the performance of the surveillance system. In multi-target scenarios, kinematic constraints from the interaction of targets with their environment or other targets can restrict target motion. Such motion constraint information is integrated by using a fixed-lag smoothing procedure, named Knowledge-Based Fixed-Lag Smoother (KB-Smoother). The proposed combination IP-MCMC-PF/KB-Smoother yields enhanced tracking

Optical based statistical space objects tracking for catalogue maintenance

The number of space objects has grown substantially in the past decades due to new launches, regular mission activities, and breakup events. This has significantly affected the space environment and the development of the space industry. To ensure safe operation of space assets, Space Situational Awareness (SSA) has attracted considerable attention in recent years. One primary strategy in SSA is to establish and maintain a Space Object Catalogue (SOC) to provide timely updated data for SSA applications, e.g., conjunction analysis, collision avoidance manoeuvring. This thesis investigates three techniques for SOC maintenance, namely the tracklet association method for initial orbit determination, the multi-target tracking method for the refinement of orbital state estimation, and multi-sensor tasking method for the optimisation of sensor resources. Generally speaking, due to the limited number of optical sensors used to track the large population of space objects, the obtained observational arcs for many targets are very short. Such short arcs, which contain a small number of angular observations, are referred as tracklets. Given such limited data, typical orbit determination methods, e.g., Laplace, Gaussian, Double-R methods, may fail to produce a valid orbital solution. By contrast, tracklet association methods compare and correlate multiple tracklets across time, and following successful association, a reliable initial orbital state can be further determined for SOC maintenance. This thesis proposes an improved initial value problem optimisation method for accurate and efficient tracklet association, and a common ellipse method to distinguish false associations of tracklets from objects in the same constellation. The proposed methods are validated using real optical data collected from the Mount Stromlo Observatory, Canberra, Australia. Furthermore, another challenging task in SSA is to track multiple objects for the maintenance of a catalog. The Bayesian multi-target tracking filter addresses this issue by associating measurements to initially known or newly detected targets and simultaneously estimating the timevarying number of targets and their orbital states. In order to achieve efficient tracking of the new space objects, a novel birth model using the Boundary Value Problem (BVP) approach is proposed. The proposed BVP birth model is implemented in the Labelled Multi-Bernoulli (LMB) filter, which is an efficient multi-target tracker developed based on the Random Finite Set (RFS) theory, for improved computational efficiency of new space object tracking. Simulation results indicate that the computational efficiency of the proposed method significantly outperforms the state-of-the-art methods. Finally, as limited sensors are available for SOC maintenance, an appropriate sensor tasking scheme is essential for the optimisation of sensor resources. The optimal sensor tasking command allocates multiple sensors to take the best action and produce useful measurements for more accurate orbital state estimation. In this thesis, an analytical form is derived for the Rényi divergence of LMB RFS in which each target state density is a single Gaussian component. The obtained analytical Rényi divergence is formulated as a reward function for multi-sensor tasking, which improves the computational efficiency, especially for large-scale space object tracking. In addition, this thesis further investigates the benefits of using the analytical Rényi  divergence and various space-based and ground-based sensor networks for accurate tracking of objects in geosynchronous Earth orbit

Random Finite Sets Based Very Short-Term Solar Power Forecasting Through Cloud Tracking

Tracking clouds with a sky camera within a very short horizon below thirty seconds can be a solution to mitigate the effects of sunlight disruptions. A Probability Hypothesis Density (PHD) filter and a Cardinalised Probability Hypothesis Density (CPHD) filter were used on a set of pre-processed sky images. Both filters have been compared with the state-of-the-art methods for performance. It was found that both filters are suitable to perform very-short term irradiance forecasting

Fusion of Data from Heterogeneous Sensors with Distributed Fields of View and Situation Evaluation for Advanced Driver Assistance Systems

In order to develop a driver assistance system for pedestrian protection, pedestrians in the environment of a truck are detected by radars and a camera and are tracked across distributed fields of view using a Joint Integrated Probabilistic Data Association filter. A robust approach for prediction of the system vehicles trajectory is presented. It serves the computation of a probabilistic collision risk based on reachable sets where different sources of uncertainty are taken into account

Intelligent video surveillance

In the focus of this thesis are the new and modified algorithms for object detection, recognition and tracking within the context of video analytics. The manual video surveillance has been proven to have low effectiveness and, at the same time, high expense because of the need in manual labour of operators, which are additionally prone to erroneous decisions. Along with increase of the number of surveillance cameras, there is a strong need to push for automatisation of the video analytics. The benefits of this approach can be found both in military and civilian applications. For military applications, it can help in localisation and tracking of objects of interest. For civilian applications, the similar object localisation procedures can make the criminal investigations more effective, extracting the meaningful data from the massive video footage. Recently, the wide accessibility of consumer unmanned aerial vehicles has become a new threat as even the simplest and cheapest airborne vessels can carry some cargo that means they can be upgraded to a serious weapon. Additionally they can be used for spying that imposes a threat to a private life. The autonomous car driving systems are now impossible without applying machine vision methods. The industrial applications require automatic quality control, including non-destructive methods and particularly methods based on the video analysis. All these applications give a strong evidence in a practical need in machine vision algorithms for object detection, tracking and classification and gave a reason for writing this thesis. The contributions to knowledge of the thesis consist of two main parts: video tracking and object detection and recognition, unified by the common idea of its applicability to video analytics problems. The novel algorithms for object detection and tracking, described in this thesis, are unsupervised and have only a small number of parameters. The approach is based on rigid motion segmentation by Bayesian filtering. The Bayesian filter, which was proposed specially for this method and contributes to its novelty, is formulated as a generic approach, and then applied to the video analytics problems. The method is augmented with optional object coordinate estimation using plain two-dimensional terrain assumption which gives a basis for the algorithm usage inside larger sensor data fusion models. The proposed approach for object detection and classification is based on the evolving systems concept and the new Typicality-Eccentricity Data Analytics (TEDA) framework. The methods are capable of solving classical problems of data mining: clustering, classification, and regression. The methods are proposed in a domain-independent way and are capable of addressing shift and drift of the data streams. Examples are given for the clustering and classification of the imagery data. For all the developed algorithms, the experiments have shown sustainable results on the testing data. The practical applications of the proposed algorithms are carefully examined and tested

Maximum likelihood parameter estimation in time series models using sequential Monte Carlo

Time series models are used to characterise uncertainty in many real-world dynamical phenomena. A time series model typically contains a static variable, called parameter, which parametrizes the joint law of the random variables involved in the definition of the model. When a time series model is to be fitted to some sequentially observed data, it is essential to decide on the value of the parameter that describes the data best, a procedure generally called parameter estimation. This thesis comprises novel contributions to the methodology on parameter estimation in time series models. Our primary interest is online estimation, although batch estimation is also considered. The developed methods are based on batch and online versions of expectation-maximisation (EM) and gradient ascent, two widely popular algorithms for maximum likelihood estimation (MLE). In the last two decades, the range of statistical models where parameter estimation can be performed has been significantly extended with the development of Monte Carlo methods. We provide contribution to the field in a similar manner, namely by combining EM and gradient ascent algorithms with sequential Monte Carlo (SMC) techniques. The time series models we investigate are widely used in statistical and engineering applications. The original work of this thesis is organised in Chapters 4 to 7. Chapter 4 contains an online EM algorithm using SMC for MLE in changepoint models, which are widely used to model heterogeneity in sequential data. In Chapter 5, we present batch and online EM algorithms using SMC for MLE in linear Gaussian multiple target tracking models. Chapter 6 contains a novel methodology for implementing MLE in a hidden Markov model having intractable probability densities for its observations. Finally, in Chapter 7 we formulate the nonnegative matrix factorisation problem as MLE in a specific hidden Markov model and propose online EM algorithms using SMC to perform MLE

Application of data and information fusion

Ph.DDOCTOR OF PHILOSOPH

Acoustic source localisation and tracking using microphone arrays

This thesis considers the domain of acoustic source localisation and tracking in an indoor environment. Acoustic tracking has applications in security, human-computer interaction, and the diarisation of meetings. Source localisation and tracking is typically a computationally expensive task, making it hard to process on-line, especially as the number of speakers to track increases. Much of the literature considers single-source localisation, however a practical system must be able to cope with multiple speakers, possibly active simultaneously, without knowing beforehand how many speakers are present. Techniques are explored for reducing the computational requirements of an acoustic localisation system. Techniques to localise and track multiple active sources are also explored, and developed to be more computationally efficient than the current state of the art algorithms, whilst being able to track more speakers. The first contribution is the modification of a recent single-speaker source localisation technique, which improves the localisation speed. This is achieved by formalising the implicit assumption by the modified algorithm that speaker height is uniformly distributed on the vertical axis. Estimating height information effectively reduces the search space where speakers have previously been detected, but who may have moved over the horizontal-plane, and are unlikely to have significantly changed height. This is developed to allow multiple non-simultaneously active sources to be located. This is applicable when the system is given information from a secondary source such as a set of cameras allowing the efficient identification of active speakers rather than just the locations of people in the environment. The next contribution of the thesis is the application of a particle swarm technique to significantly further decrease the computational cost of localising a single source in an indoor environment, compared the state of the art. Several variants of the particle swarm technique are explored, including novel variants designed specifically for localising acoustic sources. Each method is characterised in terms of its computational complexity as well as the average localisation error. The techniques’ responses to acoustic noise are also considered, and they are found to be robust. A further contribution is made by using multi-optima swarm techniques to localise multiple simultaneously active sources. This makes use of techniques which extend the single-source particle swarm techniques to finding multiple optima of the acoustic objective function. Several techniques are investigated and their performance in terms of localisation accuracy and computational complexity is characterised. Consideration is also given to how these metrics change when an increasing number of active speakers are to be localised. Finally, the application of the multi-optima localisation methods as an input to a multi-target tracking system is presented. Tracking multiple speakers is a more complex task than tracking single acoustic source, as observations of audio activity must be associated in some way with distinct speakers. The tracker used is known to be a relatively efficient technique, and the nature of the multi-optima output format is modified to allow the application of this technique to the task of speaker tracking

Investigating disease associated immune signatures in Diffuse Large B-cell Lymphoma.

PhD Theses MedicalDiffuse large B-cell lymphoma (DLBCL) not otherwise specified (NOS) is the most frequent subtype of lymphoma with approximately 4,800 new cases per year in the UK. Although it is a curable disease with standard immunochemotherapy, up to one third of patients are primary refractory or relapse after a period of remission. Until recently the prognosis for these patients was extremely poor. The recent approval of chimeric antigen receptor T (CAR-T) cell therapy has significantly improved the outlook for this group, however over half of the patients treated will progress and many others will not be suitable due to rapid disease progression. There are currently a multitude of new agents in development which hold much promise and will likely improve the outlook further for the highest risk patients. However, there remain several unmet needs including improved translation of biological insights to directly benefit patient care. Recent studies have focused on the genomic landscape in DLBCL, with new subgroups proposed based on the presence of recurrent and potentially actionable mutations, including many which facilitate escape from immune detection. In addition to molecular signals from the malignant lymphoma cells, there are reproducible signals from the nonmalignant compartment in both the tissue and peripheral blood microenvironment, with relevance to disease biology. Considerable variation is seen in immune cell composition and function between individuals in both health and disease, but this has not been well characterised in DLBCL. We focused predominantly on the peripheral blood immune compartment in this work, confirming the presence of a relative monocytosis and lymphopenia in DLBCL and their relevance to survival. We present a detailed description of the immune landscape in DLBCL, and document disease and outcome associated immune signatures. We identify mechanisms to account for these variations, widespread cytokine dysregulation and multiple bases of immune dysfunction. We also establish monocytes as the main peripheral blood source of cytokine production in DLBCL. Finally, we establish a pipeline for detailed characterisation of the tissue immune microenvironment using imaging mass cytometry