28 research outputs found

    Enhanced particle PHD filtering for multiple human tracking

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    PhD ThesisVideo-based single human tracking has found wide application but multiple human tracking is more challenging and enhanced processing techniques are required to estimate the positions and number of targets in each frame. In this thesis, the particle probability hypothesis density (PHD) lter is therefore the focus due to its ability to estimate both localization and cardinality information related to multiple human targets. To improve the tracking performance of the particle PHD lter, a number of enhancements are proposed. The Student's-t distribution is employed within the state and measurement models of the PHD lter to replace the Gaussian distribution because of its heavier tails, and thereby better predict particles with larger amplitudes. Moreover, the variational Bayesian approach is utilized to estimate the relationship between the measurement noise covariance matrix and the state model, and a joint multi-dimensioned Student's-t distribution is exploited. In order to obtain more observable measurements, a backward retrodiction step is employed to increase the measurement set, building upon the concept of a smoothing algorithm. To make further improvement, an adaptive step is used to combine the forward ltering and backward retrodiction ltering operations through the similarities of measurements achieved over discrete time. As such, the errors in the delayed measurements generated by false alarms and environment noise are avoided. In the nal work, information describing human behaviour is employed iv Abstract v to aid particle sampling in the prediction step of the particle PHD lter, which is captured in a social force model. A novel social force model is proposed based on the exponential function. Furthermore, a Markov Chain Monte Carlo (MCMC) step is utilized to resample the predicted particles, and the acceptance ratio is calculated by the results from the social force model to achieve more robust prediction. Then, a one class support vector machine (OCSVM) is applied in the measurement model of the PHD lter, trained on human features, to mitigate noise from the environment and to achieve better tracking performance. The proposed improvements of the particle PHD lters are evaluated with benchmark datasets such as the CAVIAR, PETS2009 and TUD datasets and assessed with quantitative and global evaluation measures, and are compared with state-of-the-art techniques to con rm the improvement of multiple human tracking performance

    Utilising Visual Attention Cues for Vehicle Detection and Tracking

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    Advanced Driver-Assistance Systems (ADAS) have been attracting attention from many researchers. Vision-based sensors are the closest way to emulate human driver visual behavior while driving. In this paper, we explore possible ways to use visual attention (saliency) for object detection and tracking. We investigate: 1) How a visual attention map such as a \emph{subjectness} attention or saliency map and an \emph{objectness} attention map can facilitate region proposal generation in a 2-stage object detector; 2) How a visual attention map can be used for tracking multiple objects. We propose a neural network that can simultaneously detect objects as and generate objectness and subjectness maps to save computational power. We further exploit the visual attention map during tracking using a sequential Monte Carlo probability hypothesis density (PHD) filter. The experiments are conducted on KITTI and DETRAC datasets. The use of visual attention and hierarchical features has shown a considerable improvement of ā‰ˆ\approx8\% in object detection which effectively increased tracking performance by ā‰ˆ\approx4\% on KITTI dataset.Comment: Accepted in ICPR202

    Audioā€Visual Speaker Tracking

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    Target motion tracking found its application in interdisciplinary fields, including but not limited to surveillance and security, forensic science, intelligent transportation system, driving assistance, monitoring prohibited area, medical science, robotics, action and expression recognition, individual speaker discrimination in multiā€speaker environments and video conferencing in the fields of computer vision and signal processing. Among these applications, speaker tracking in enclosed spaces has been gaining relevance due to the widespread advances of devices and technologies and the necessity for seamless solutions in realā€time tracking and localization of speakers. However, speaker tracking is a challenging task in realā€life scenarios as several distinctive issues influence the tracking process, such as occlusions and an unknown number of speakers. One approach to overcome these issues is to use multiā€modal information, as it conveys complementary information about the state of the speakers compared to singleā€modal tracking. To use multiā€modal information, several approaches have been proposed which can be classified into two categories, namely deterministic and stochastic. This chapter aims at providing multimedia researchers with a stateā€ofā€theā€art overview of tracking methods, which are used for combining multiple modalities to accomplish various multimedia analysis tasks, classifying them into different categories and listing new and future trends in this field

    Multiple Object Trajectory Estimation Using Backward Simulation

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    This paper presents a general solution for computing the multi-object posterior for sets of trajectories from a sequence of multi-object (unlabelled) filtering densities and a multi-object dynamic model. Importantly, the proposed solution opens an avenue of trajectory estimation possibilities for multi-object filters that do not explicitly estimate trajectories. In this paper, we first derive a general multi-trajectory backward smoothing equation based on random finite sets of trajectories. Then we show how to sample sets of trajectories using backward simulation for Poisson multi-Bernoulli filtering densities, and develop a tractable implementation based on ranked assignment. The performance of the resulting multi-trajectory particle smoothers is evaluated in a simulation study, and the results demonstrate that they have superior performance in comparison to several state-of-the-art multi-object filters and smoothers.Comment: Accepted for publication in IEEE Transactions on Signal Processin

    Novel methods for multi-target tracking with applications in sensor registration and fusion

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    Maintaining surveillance over vast volumes of space is an increasingly important capability for the defence industry. A clearer and more accurate picture of a surveillance region could be obtained through sensor fusion between a network of sensors. However, this accurate picture is dependent on the sensor registration being resolved. Any inaccuracies in sensor location or orientation can manifest themselves into the sensor measurements that are used in the fusion process, and lead to poor target tracking performance. Solutions previously proposed in the literature for the sensor registration problem have been based on a number of assumptions that do not always hold in practice, such as having a synchronous network and having small, static registration errors. This thesis will propose a number of solutions to resolving the sensor registration and sensor fusion problems jointly in an efficient manner. The assumptions made in previous works will be loosened or removed, making the solutions more applicable to problems that we are likely to see in practice. The proposed methods will be applied to both simulated data, and a segment of data taken from a live trial in the field

    A Review on Human Activity Recognition Using Vision-Based Method

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    Agent-based modeling for environmental management. Case study: virus dynamics affecting Norwegian fish farming in fjords

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    Background: Norwegian fish-farming industry is an important industry, rapidly growing, and facing significant challenges such as the spread of pathogens1, trade-off between locations, fish production and health. There is a need for research, i.e. the development of theories (models), methods, techniques and tools for analysis, prediction and management, i.e. strategy development, policy design and decision making, to facilitate a sustainable industry. Loss due to the disease outbreaks in the aquaculture systems pose a large risk to a sustainable fish industry system, and pose a risk to the coastal and fjord ecosystem systems as a whole. Norwegian marine aquaculture systems are located in open areas (i.e. fjords) where they overlap and interact with other systems (e.g. transport, wild life, tourist, etc.). For instance, shedding viruses from aquaculture sites affect the wild fish in the whole fjord system. Fish disease spread and pathogen transmission in such complex systems, is process that it is difficult to predict, analyze, and control. There are several time-variant factors such as fish density, environmental conditions and other biological factors that affect the spread process. In this thesis, we developed methods to examine these factors on fish disease spread in fish populations and on pathogen spread in the time-space domain. Then we develop methods to control and manage the aquaculture system by finding optimal system settings in order to have a minimum infection risk and a high production capacity. Aim: The overall objective of the thesis is to develop agent-based models, methods and tools to facilitate the management of aquaculture production in Norwegian fjords by predicting the pathogen dynamics, distribution, and transmission in marine aquaculture systems. Specifically, the objectives are to assess agent-based modeling as an approach to understanding fish disease spread processes, to develop agent-based models that help us predict, analyze and understand disease dynamics in the context of various scenarios, and to develop a framework to optimize the location and the load of the aquaculture systems so as to minimize the infection risk in a growing fish industry. Methods: We use agent-based method to build models to simulate disease dynamics in fish populations and to simulate pathogen transmission between several aquaculture sites in a Norwegian fjord. Also, we use particle swarm optimization algorithm to identify agent-based modelsā€™ parameters so as to optimize the dynamics of the system model. In this context, we present a framework for using a particle swarm optimization algorithm to identify the parameter values of the agent-based model of aquaculture system that are expected to yield the optimal fish densities and farm locations that avoid the risk of spreading disease. The use of particle swarm optimization algorithm helps in identifying optimal agent-based modelsā€™ input parameters depending on the feedback from the agentbased modelsā€™ outputs. Results: As the thesis is built on three main studies, the results of the thesis work can be divided into three components. In the first study, we developed many agent-based models to simulate fish disease spread in stand-alone fish populations. We test the models in different scenarios by varying the agents (i.e. fish and pathogens) parameters, environment parameters (i.e. seawater temperature and currents), and interactions (interaction between agents-agents, and agents-environment) parameters. We use sensitivity analysis method to test different key input parameters such as fish density, fish swimming behavior, seawater temperature, and sea currents to show their effects on the disease spread process. Exploring the sensitivity of fish disease dynamics to these key parameters helps in combatting fish disease spread. In the second study, we build infection risk maps in a space-time domain, by developing agent-based models to identify the pathogen transmission patterns. The agent-based method helps us advance our understanding of pathogen transmission and builds risk maps to help us reduce the spread of infectious fish diseases. By using this method, we may study the spatial and dynamic aspects of the spread of infections and address the stochastic nature of the infection process. In the third study, we developed a framework for the optimization of the aquaculture systems. The framework uses particle swarm optimization algorithm to optimize agent-based modelsā€™ parameters so as to optimize the objective function. The framework was tested by developing a model to find optimal fish densities and farm locations in marine aquaculture system in a Norwegian fjord. Results show so that the rapid convergence of the presented particle swarm optimization algorithm to the optimal solution, - the algorithm requires a maximum of 18 iterations to find the best solution which can increase the fish density to three times while keeping the risk of infection at an accepted level. Conclusion: There are many contributions of this research work. First, we assessed the agent-based modeling as a method to simulate and analyze fish disease spread dynamics as a foundation for managing aquaculture systems. Results from this study demonstrate how effective the use of agentbased method is in the simulation of infectious diseases. By using this method, we are able to study spatial aspects of the spread of fish diseases and address the stochastic nature of infections process. Agent-based models are flexible, and they can include many external factors that affect fish disease dynamics such as interactions with wild fish and ship traffic. Agent-based models successfully help us to overcome the problem associated with lack of data in fish disease transmission and contribute to our understanding of different cause-effects relationships in the dynamics of fish diseases. Secondly, we developed methods to build infection risk maps in a space-time domain conditioned upon the identification of the pathogen transmission patterns in such a space-time domain, so as to help prevent and, if needed, combat infectious fish diseases by informing the management of the fish industry in Norway. Finally, we developed a method by which we may optimize the fish densities and farm locations of aquaculture systems so as to ensure a sustainable fish industry with a minimum risk of infection and a high production capacity. This PhD study offers new research-based approaches, models and tools for analysis, predictions and management that can be used to facilitate a sustainable development of the marine aquaculture industry with a maximal economic outcome and a minimal environmental impact

    Nonlinear State Estimation Using Optimal Gaussian Sampling with Applications to Tracking

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    This thesis is concerned with the ubiquitous problem of estimating the hidden state of a discrete-time stochastic nonlinear dynamic system. The focus is on the derivation of new Gaussian state estimators and the improvement of existing approaches. Also the challenging task of distributed state estimation is addressed by proposing a sample-based fusion of local state estimates. The proposed estimation techniques are applied to extended object tracking

    Testing the predictive coding account of temporal integration in the human visual system : a computational and behavioural study

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    A major goal of vision science is to understand how the visual system maintains behaviourally relevant perceptions given the level of uncertainty in the signals it receives. One proposed solution is that the visual system applies predictive coding to its inputs based on the integration of prior knowledge and current stimulus features. However, support for some vital aspects of predictive coding in the temporal domain is lacking and simpler accounts of temporal integration also exist. The aim of this thesis was to test two key attributes of predictive coding in time a) does the visual system apply adaptive weighting to prediction errors and b) can the visual system apply probabilistic information learnt from stimulus sequences when making predictions. In chapters 3 & 4, we tested predictive codingā€™s ideas of how prediction errors are weighted under the theoretical guidance of a temporal integration model linked to predictive processing, called the Kalman filter. Here, both experiments supported predictive coding. We showed that, consistent with the Kalman filter, visual estimates and the way estimation errors were corrected, adapted to stimulus behaviour and viewing conditions. In chapter 5, we assessed the ability of the visual system to integrate conditional relationships present in sequences of stimuli when making predictions. To do this, we inserted a stimulus sequence that changed and omitted trials based on Markov transition probabilities that made some transitions more or less probable and assessed reaction times and omission trial responses. Reaction time data was consistent with predictive coding, in that more predictable changes elicited faster responses. Omission trials data, was though, less clear. When faced with no stimulus, participants did not apply the conditional probabilities in their decisions optimally, instead applying non optimal decision strategies, inconsistent with predictive coding. In summary, this thesis supports the predictive coding of temporal integration but questions its application in all situations

    Modelling and Identification of Immune Cell Migration during the Inflammatory Response

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    Neutrophils are the white blood cells that play a crucial role in the response of the innate immune system to tissue injuries or infectious threats. Their rapid arrival to the damaged area and timely removal from it define the success of the inflammatory process. Therefore, understanding neutrophil migratory behaviour is essential for the therapeutic regulation of multiple inflammation-mediated diseases. Recent years saw rapid development of various in vivo models of inflammation that provide a remarkable insight into the neutrophil function. The main drawback of the \textit{in vivo} microscopy is that it usually focuses on the moving cells and obscures the external environment that drives their migration. To evaluate the effect of a particular treatment strategy on neutrophil behaviour, it is necessary to recover the information about the cell responsiveness and the complex extracellular environment from the limited experimental data. This thesis addresses the presented inference problem by developing a dynamical modelling and estimation framework that quantifies the relationship between an individual migrating cell and the global environment. \par The first part of the thesis is concerned with the estimation of the hidden chemical environment that modulates the observed cell migration during the inflammatory response in the injured tail fin of zebrafish larvae. First, a dynamical model of the neutrophil responding to the chemoattractant concentration is developed based on the potential field paradigm of object-environment interaction. This representation serves as a foundation for a hybrid model that is proposed to account for heterogeneous behaviour of an individual cell throughout the migration process. An approximate maximum likelihood estimation framework is derived to estimate the hidden environment and the states of multiple hybrid systems simultaneously. The developed framework is then used to analyse the neutrophil tracking data observed in vivo under the assumption that each neutrophil at each time can be in one of three migratory modes: responding to the environment, randomly moving, and stationary. The second part of the thesis examines the process of neutrophil migration at the subcellular scale, focusing on the subcellular mechanism that translates the local environment sensing into the cell shape change. A state space model is formulated based on the hypothesis that links the local protrusions of the cell membrane and the concentration of the intracellular pro-inflammatory signalling protein. The developed model is tested against the local concentration data extracted from the in vivo time-lapse images via the classical expectation-maximisation algorithm
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