This dissertation concerns with the formulation of an improved multi-target multi-Bernoulli (MeMBer) filter and the use of the joint multi-target (JoM) estimator in an effective and efficient manner for a specific implementation of MeMBer filters. After reviewing random finite set (RFS) formalism for multi-target tracking problems and the related Bayes estimators the major contributions of this dissertation are explained in detail. The second chapter of this dissertation is dedicated to the analysis of the relationship between the multi-Bernoulli RFS distribution and the MeMBer corrector. This analysis leads to the formulation of an unbiased MeMBer filter without making any limiting assumption. Hence, as opposed to the popular cardinality balanced multi-target multi-Bernoulli (CBMeMBer) filter, the proposed MeMBer filter can be employed under the cases when sensor detection probability is moderate to low. Furthermore, a statistical refinement process is introduced to improve the stability of the estimated cardinality of targets obtained from the proposed MeMBer filter. The results from simulations demonstrate the effectiveness of the improved MeMBer filter. In Chapters III and IV, the Bayesian optimal estimators proposed for the RFS based multi-target tracking filters are examined in detail. First, an optimal solution to the unknown constant in the definition of the JoM estimator is determined by solving a multi-objective optimization problem. Thus, the JoM estimator can be implemented for tracking of a Bernoulli target using the optimal joint target detection and tracking (JoTT) filter. The results from simulations confirm assertions about its performance obtained by theoretical analysis in the literature. Finally, in the third chapter of this dissertation, the proposed JoM estimator is reformulated for RFS multi-Bernoulli distributions. Hence, an effective and efficient implementation of the JoM estimator is proposed for the Gaussian mixture implementations of the MeMBer filters. Simulation results demonstrate the robustness of the proposed JoM estimator under low-observable conditions. / Thesis / Doctor of Philosophy (PhD)

Multi-target tracking is the process that allows the extraction of object motion patterns of interest from a scene. Motion patterns are often described through metadata representing object locations and shape information. In the first part of this thesis we discuss the state-of-the-art methods aimed at accomplishing this task on monocular views and also analyse the methods for evaluating their performance. The second part of the thesis describes our research contribution to these topics. We begin presenting a method for multi-target tracking based on track-before-detect (MTTBD) formulated as a particle filter. The novelty involves the inclusion of the target identity (ID) into the particle state, which enables the algorithm to deal with an unknown and unlimited number of targets. We propose a probabilistic model of particle birth and death based on Markov Random Fields. This model allows us to overcome the problem of the mixing of IDs of close targets. We then propose three evaluation measures that take into account target-size variations, combine accuracy and cardinality errors, quantify long-term tracking accuracy at different accuracy levels, and evaluate ID changes relative to the duration of the track in which they occur. This set of measures does not require pre-setting of parameters and allows one to holistically evaluate tracking performance in an application-independent manner. Lastly, we present a framework for multi-target localisation applied on scenes with a high density of compact objects. Candidate target locations are initially generated by extracting object features from intensity maps using an iterative method based on a gradient-climbing technique and an isocontour slicing approach. A graph-based data association method for multi-target tracking is then applied to link valid candidate target locations over time and to discard those which are spurious. This method can deal with point targets having indistinguishable appearance and unpredictable motion. MT-TBD is evaluated and compared with state-of-the-art methods on real-world surveillance.

abstract: Technological advances have enabled the generation and collection of various data from complex systems, thus, creating ample opportunity to integrate knowledge in many decision making applications. This dissertation introduces holistic learning as the integration of a comprehensive set of relationships that are used towards the learning objective. The holistic view of the problem allows for richer learning from data and, thereby, improves decision making. The first topic of this dissertation is the prediction of several target attributes using a common set of predictor attributes. In a holistic learning approach, the relationships between target attributes are embedded into the learning algorithm created in this dissertation. Specifically, a novel tree based ensemble that leverages the relationships between target attributes towards constructing a diverse, yet strong, model is proposed. The method is justified through its connection to existing methods and experimental evaluations on synthetic and real data. The second topic pertains to monitoring complex systems that are modeled as networks. Such systems present a rich set of attributes and relationships for which holistic learning is important. In social networks, for example, in addition to friendship ties, various attributes concerning the users' gender, age, topic of messages, time of messages, etc. are collected. A restricted form of monitoring fails to take the relationships of multiple attributes into account, whereas the holistic view embeds such relationships in the monitoring methods. The focus is on the difficult task to detect a change that might only impact a small subset of the network and only occur in a sub-region of the high-dimensional space of the network attributes. One contribution is a monitoring algorithm based on a network statistical model. Another contribution is a transactional model that transforms the task into an expedient structure for machine learning, along with a generalizable algorithm to monitor the attributed network. A learning step in this algorithm adapts to changes that may only be local to sub-regions (with a broader potential for other learning tasks). Diagnostic tools to interpret the change are provided. This robust, generalizable, holistic monitoring method is elaborated on synthetic and real networks. / Dissertation/Thesis / Doctoral Dissertation Industrial Engineering 2014

Many multiple target tracking algorithms operate in the local frame of the sensor and have difficulty with track reallocation when targets move in and out of the sensor field of view. This poses a problem when an unmanned aerial vehicle (UAV) is tracking multiple ground targets on a road network larger than its field of view. We propose a Rao-Blackwellized Particle Filter (RBPF) to maintain individual target tracks and to perform probabilistic data association when the targets are constrained to a road network. This is particularly useful when a target leaves then re-enters the UAV’s field of view. The RBPF is structured as a particle filter of particle filters. The top level filter handles data association and each of its particles maintains a bank of particle filters to handle target tracking. The tracking particle filters incorporate both positive and negative information when a measurement is received. We implement two path planning controllers, exhaustive receding horizon control (ERHC) and a neural net trained with deep reinforcement learning (Deep-RL), and compare their ability to improve the certainty for multiple target location estimates. The controllers prioritize paths that reduce each target’s entropy. While the ERHC achieved optimal stead-state estimates the DeepRL controller identified more efficient sweeping search patterns when there is limited information regarding target locations. The neural net achieves O(1) computational complexity during decision making but must first be trained on a given map. In addition, we provide a theorem that calculates the lower-bound for the average-entropy of the RBPF. Particle Filter entropy is used as a unit of measurement as it gives a way of accurately comparing the precision of complex multi-modal estimates. This gives a reliable way of establishing the resources needed to accomplish mission objectives as well as providing a reliable method of determining the effectiveness of different multi-agent path planners. Finally we outline results both in simulation and hardware. In simulation we obtained the results for our different path planners over 2000 Monte Carlo runs and show how the different path planners compare and measure up to the lower-bound of average-entropy. The results from a hardware test provide evidence that the ideas presented in this thesis hold true in an end-to-end solution.

Many multiple target tracking algorithms operate in the local frame of the sensor and have difficulty with track reallocation when targets move in and out of the sensor field of view. This poses a problem when an unmanned aerial vehicle (UAV) is tracking multiple ground targets on a road network larger than its field of view. We propose a Rao-Blackwellized Particle Filter (RBPF) to maintain individual target tracks and to perform probabilistic data association when the targets are constrained to a road network. This is particularly useful when a target leaves then re-enters the UAV's field of view. The RBPF is structured as a particle filter of particle filters. The top level filter handles data association and each of its particles maintains a bank of particle filters to handle target tracking. The tracking particle filters incorporate both positive and negative information when a measurement is received. We implement two path planning controllers, exhaustive receding horizon control (ERHC) and a neural net trained with deep reinforcement learning (Deep-RL), and compare their ability to improve the certainty for multiple target location estimates. The controllers prioritize paths that reduce each target's entropy. While the ERHC achieved optimal stead-state estimates the Deep-RL controller identified more efficient sweeping search patterns when there is limited information regarding target locations. The neural net achieves O(1) computational complexity during decision making but must first be trained on a given map. In addition, we provide a theorem that calculates the lower-bound for the average-entropy of the RBPF. Particle Filter entropy is used as a unit of measurement as it gives a way of accurately comparing the precision of complex multi-modal estimates. This gives a reliable way of establishing the resources needed to accomplish mission objectives as well as providing a reliable method of determining the effectiveness of different multi-agent path planners. Finally we outline results both in simulation and hardware. In simulation we obtained the results for our different path planners over 2000 Monte Carlo runs and show how the different path planners compare and measure up to the lower-bound of average-entropy. The results from a hardware test provide evidence that the ideas presented in this thesis hold true in an end-to-end solution.

With the recent advances in sensor technology and resulting sensor resolution, conven- tional point-based target tracking algorithms are becoming insufficient, particularly in application domains such as autonomous vehicles, visual tracking and surveillance using high resolution sensors. This has renewed the interest in extended target (ET) tracking, which aims to track not only the centroid of a target, but also its shape and size over time. This thesis addresses three of the most challenging problems in the domain of ET tracking applications. The first investigated challenge is the need for an accu- rate shape and centre estimate for the ET object with an arbitrary unknown star- convex shape in presence of non-Gaussian noise. The proposed method is based on a Student’s-t process regression algorithm which is defined in a recursive framework to be applicable for on-line tracking problems. The second problem tries to relax any constraints, including the star-convex con- straint, that is imposed on the shape of the ET object during the course of estimation by defining a novel Random Polytopes shape descriptor. Also, the proposed solution introduces a method to mitigate the troubles caused as a result of self-occlusion in ET tracking applications which its ignorance may cause catastrophic divergence in the ET state estimate.Finally, a framework for tracking multiple ET objects in the presence of clutter and occlusion is studied and a solution is proposed. The proposed method can estimate the centre and shape of the ET objects in a realistically scenario with the self- and mutual-occlusion challenges being considered. The proposed approach defines a time varying state-dependent probability of detection for each ET that enables the track to prolong even under adverse conditions caused due to mutual-occlusion. Plus, the proposed algorithm uses set-membership uncertainty models to bound the association and target shape uncertainties of occluded ET, to obtain more accurate state and shape estimates of an ET object. The performance of the proposed methods are quantified on realistically simulated scenarios with self- and mutual-occlusions and their results are compared against existing state-of-the-art methods for ET tracking applications. / Thesis / Doctor of Philosophy (PhD)

This dissertation presents solutions to two open problems in estimation theory. The first is a tractable analytical solution for problems in multi-target filtering which are too complex to solve using traditional techniques. The second explores a new approach to the nonlinear filtering problem for a general class of models. The approach to the multi-target filtering problem which involves jointly estimating a random process of the number of targets and their state, developed using the probability hypothesis density (PHD) filter alleviates the intractability of the problem by avoiding explicit data association. Moreover, the notion of linear jump Markov systems is generalized to the multiple target case to accommodate births, deaths and switching dynamics to derive a closed form solution to the PHD recursion for this so-called linear Gaussian jump Markov multi-target model. The proposed solution is general enough to accommodate a broad class of practical problems which are deemed intractable using traditional techniques. Based on this closed form solution, an efficient method is developed for tracking multiple maneuvering targets that switch between multiple models without the need for gating, track initiation and termination, or clustering for extracting state estimates. The approach to the nonlinear filtering problem explores the framework of the virtual linear fractional transformation (LFT) model which localizes the nonlinearity to the feedback with a simple and sparse structure. The LFT is an exact representation for any differentiable nonlinear mapping and therefore amenable to a general class of problems. An alternative analytical approximation method is presented which avoids linearization of the state space model. The uncorrelated structure of the feedback connection gives of the state space model. The uncorrelated structure of the feedback connection gives better second-order moment approximation of the nonlinearly mapped variables. By arranging the unscented transform in the feedback, the prediction and estimation steps are derived in closed form. The proposed filters for the discrete-time model and continuous-time dynamics with sampled-data measurements respectively are shown to be robust under highly nonlinear and uncertain conditions where standard analytical approximation based filters diverge. Moreover, the LFT based filters are efficient for online implementation. In addition, the LFT framework is applied to extend the closed form solution of the PHD recursion to the nonlinear jump Markov multi-target model.

This thesis deals with the problem of online visual tracking of multiple humans in an enclosed environment. The focus is to develop techniques to deal with the challenges of varying number of targets, inter-target occlusions and interactions when every target gives rise to multiple measurements (pixels) in every video frame. This thesis contains three different contributions to the research in multi-target tracking. Firstly, a multiple target tracking algorithm is proposed which focuses on mitigating the inter-target occlusion problem during complex interactions. This is achieved with the help of a particle filter, multiple video cues and a new interaction model. A Markov chain Monte Carlo particle filter (MCMC-PF) is used along with a new interaction model which helps in modeling interactions of multiple targets. This helps to overcome tracking failures due to occlusions. A new weighted Markov chain Monte Carlo (WMCMC) sampling technique is also proposed which assists in achieving a reduced tracking error. Although effective, to accommodate multiple measurements (pixels) produced by every target, this technique aggregates measurements into features which results in information loss. In the second contribution, a novel variational Bayesian clustering-based multi-target tracking framework is proposed which can associate multiple measurements to every target without aggregating them into features. It copes with complex inter-target occlusions by maintaining the identity of targets during their close physical interactions and handles efficiently a time-varying number of targets. The proposed multi-target tracking framework consists of background subtraction, clustering, data association and particle filtering. A variational Bayesian clustering technique groups the extracted foreground measurements while an improved feature based joint probabilistic data association filter (JPDAF) is developed to associate clusters of measurements to every target. The data association information is used within the particle filter to track multiple targets. The clustering results are further utilised to estimate the number of targets. The proposed technique improves the tracking accuracy. However, the proposed features based JPDAF technique results in an exponential growth of computational complexity of the overall framework with increase in number of targets. In the final work, a novel data association technique for multi-target tracking is proposed which more efficiently assigns multiple measurements to every target, with a reduced computational complexity. A belief propagation (BP) based cluster to target association method is proposed which exploits the inter-cluster dependency information. Both location and features of clusters are used to re-identify the targets when they emerge from occlusions. The proposed techniques are evaluated on benchmark data sets and their performance is compared with state-of-the-art techniques by using, quantitative and global performance measures.

With the incidence of Alzheimer’s disease (AD) growing worldwide and in Canada along with the growing economic and social burdens, the need for more effective therapies becomes of great importance. Since the discovery of AD, a number of proposed theories have arisen to explain the pathophysiology including the i) cholinergic theory, ii) oxidative stress pathways, and iii) metal ion imbalance. The major class of drug therapies to treat AD are cholinesterase inhibitors; however, the “one drug, one target” approach has not proven fruitful and generally becomes ineffective in later stages of disease progression. In this project, we synthesized a library of 1,2,3,4-tetrahydroacridine derivatives (10a-d, 11a-e, 12a-e, and 13a-f) as potential agents to target the cholinergic and oxidative stress pathways of AD. Chapter I provides background information on the role of AChE and BuChE enzymes in AD. Furthermore, this chapter describes the neurotoxicity of reactive oxygen species (ROS) and metals in AD. Chapter II provides a summary of project hypothesis and rationale. Chapter III describes the synthetic details regarding the synthesis of target small molecules. It further describes the principles involved in carrying out biological evaluation such as AChE and BuChE inhibition, antioxidant properties via DPPH stable radical scavenging, iron chelation capacity using ferrozine and in vitro cell viability data in neuroblastoma cells. Chapter IV describes the SAR details on ChE inhibition, antioxidant activities, iron chelation and cell viability profiles and molecular modeling details. A brief conclusion and future directions are included in Chapter V and the final section, Chapter VI provides experimental details for synthetic chemistry including analytical data of synthesized compounds and protocols for biological evaluations. This study identified novel tetrahydroacridine derivatives with nanomolar inhibition of both human AChE and human BuChE enzymes that were more potent relative to the reference agent tacrine. Compound 10d[N-(3,4-dimethoxybenzyl)-1,2,3,4-tetrahydroacridin-9-amine] was identified as a potent inhibitor of BuChE (IC50 = 24.0 nM) and compound 13c [6-chloro-N-(pyridine- 2-ylmethyl)-1,2,3,4-tetrahydroacridin-9-amine] was identified as a potent inhibitor of AChE (IC50 = 95.0 nM) with good inhibition of BuChE (IC50 = 1.61 μM) whereas compound 11e [6-chloro-N-(3,4-dimethoxybenzyl)-1,2,3,4-tetrahydroacridin-9-amine] was identified with an optimum combination of dual AChE and BuChE inhibition (AChE IC50 = 0.9 μM; BuChE IC50= 1.4 μM). In conclusion, our studies provide new insight into the design and development of novel tetrahydroacridine derivatives to target multiple pathological routes of AD.

We propose a framework for detecting and tracking multiple interacting objects from a single, static, uncalibrated camera. The number of objects is variable and unknown, and object-class-specific models are not available. We use background subtraction results as measurements for object detection and tracking. Given these constraints, the main challenge is to associate pixel measurements with (possibly interacting) object targets. We first track clusters of pixels, and note when they merge or split. We then build an inference graph, representing relations between the tracked clusters. Using this graph and a generic object model based on spatial connectedness and coherent motion, we label the tracked clusters as whole objects, fragments of objects or groups of interacting objects. The outputs of our algorithm are entire tracks of objects, which may include corresponding tracks from groups of objects during interactions. Experimental results on multiple video sequences are shown.