Search Results - "Gning, Amadou"

Overview of Bayesian sequential Monte Carlo methods for group and extended object tracking by Mihaylova, Lyudmila, Carmi, Avishy Y., Septier, François, Gning, Amadou, Pang, Sze Kim, Godsill, Simon

“…This work presents the current state-of-the-art in techniques for tracking a number of objects moving in a coordinated and interacting fashion. Groups are…”
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Information Rich Voxel Grid for Use in Heterogeneous Multi-Agent Robotics by Balding, Steven, Gning, Amadou, Cheng, Yongqiang, Iqbal, Jamshed

“…Robotic agents are now ubiquitous in both home and work environments; moreover, the degree of task complexity they can undertake is also increasing…”
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Localisation of an unknown number of land mines using a network of vapour detectors by Chhadé, Hiba Haj, Abdallah, Fahed, Mougharbel, Imad, Gning, Amadou, Julier, Simon, Mihaylova, Lyudmila

“…We consider the problem of localising an unknown number of land mines using concentration information provided by a wireless sensor network. A number of vapour…”
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Box particle filtering for nonlinear state estimation using interval analysis by Abdallah, Fahed, Gning, Amadou, Bonnifait, Philippe

“…In recent years particle filters have been applied to a variety of state estimation problems. A particle filter is a sequential Monte Carlo Bayesian estimator…”
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A Box Particle Filter Method for Tracking Multiple Extended Objects by De Freitas, Allan, Mihaylova, Lyudmila, Gning, Amadou, Schikora, Marek, Ulmke, Martin, Angelova, Donka, Koch, Wolfgang

“…Extended objects generate a variable number of multiple measurements. In contrast with point targets, extended objects are characterized with their size or…”
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Autonomous crowds tracking with box particle filtering and convolution particle filtering by De Freitas, Allan, Mihaylova, Lyudmila, Gning, Amadou, Angelova, Donka, Kadirkamanathan, Visakan

“…Autonomous systems such as Unmanned Aerial Vehicles (UAVs) need to be able to recognise and track crowds of people, e.g. for rescuing and surveillance…”
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Bernoulli Particle/Box-Particle Filters for Detection and Tracking in the Presence of Triple Measurement Uncertainty by Gning, A., Ristic, B., Mihaylova, L.

“…This work presents sequential Bayesian detection and estimation methods for nonlinear dynamic stochastic systems using measurements affected by three sources…”
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Box-particle probability hypothesis density filtering by Schikora, Marek, Gning, Amadou, Mihaylova, Lyudmila, Cremers, Daniel, Koch, Wolfgang

“…This paper develops a novel approach for multitarget tracking, called box-particle probability hypothesis density filter (box-PHD filter). The approach is able…”
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Group Object Structure and State Estimation With Evolving Networks and Monte Carlo Methods by Gning, Amadou, Mihaylova, Lyudmila, Maskell, Simon, Sze Kim Pang, Godsill, Simon

“…This paper proposes a technique for motion estimation of groups of targets based on evolving graph networks. The main novelty over alternative group tracking…”
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Parallelized Particle and Gaussian Sum Particle Filters for Large-Scale Freeway Traffic Systems by Mihaylova, L., Hegyi, A., Gning, A., Boel, R. K.

“…Large-scale traffic systems require techniques that are able to 1) deal with high amounts of data and heterogenous data coming from different types of sensors,…”
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Interval Macroscopic Models for Traffic Networks by Gning, A, Mihaylova, L, Boel, R K

“…The development of real-time traffic models is of paramount importance for the purposes of optimizing traffic flow. Inspired by the compositional model (CM)…”
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Non Parametric Distributed Inference in Sensor Networks Using Box Particles Messages by Haj Chhadé, Hiba, Gning, Amadou, Abdallah, Fahed, Mougharbel, Imad, Julier, Simon

“…This paper deals with the problem of inference in distributed systems where the probability model is stored in a distributed fashion. Graphical models provide…”
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Extended object tracking with convolution particle filtering by Angelova, D., Mihaylova, L., Petrov, N., Gning, A.

“…This paper proposes a sequential Monte Carlo filter (particle filter) for state and parameter estimation of dynamic systems. It is applied to the problem of…”
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Bernoulli filtering on a moving platform by Julier, Simon J., Gning, Amadou

“…This paper considers the problem of tracking a target - which might or might not exist - from a platform whose position is not known perfectly and might…”
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An interval compositional vehicular traffic model for real-time applications by Gning, Amadou, Mihaylova, Lyudmila, Boel, Rene

“…This paper proposes an interval approach to vehicular traffic flow modeling. The developed interval compositional model (ICM) provides a natural way of…”
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Adapting Particle Filter on Interval Data for Dynamic State Estimation by Abdallah, F., Gning, A., Bonnifait, P.

“…Over the last years, particle filters (PF) have attracted considerable attention in the field of nonlinear state estimation due to their relaxation of the…”
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Crowd tracking with box particle filtering by Petrov, Nikolay, Mihaylova, Lyudmila, de Freitas, Allan, Gning, Amadou

“…This paper focuses on tracking large groups of objects, such as crowds of pedestrians. Large groups generate multiple measurements with uncertain origin…”
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Non-linear state estimation using imprecise samples by Gning, Amadou, Julier, Simon, Mihaylova, Lyudmila

“…In state estimation theory, the general formulation is often done under assumptions of stochastic noise processes obeying well known probability distributions…”
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A novel Sequential Monte Carlo approach for extended object tracking based on border parameterisation by Petrov, N., Mihaylova, L., Gning, A., Angelova, D.

“…Extended objects are characterised with multiple measurements originated from different locations of the object surface. This paper presents a novel Sequential…”
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Autonomous Crowds Tracking with Box Particle Filtering and Convolution Particle Filtering by De Freitas, Allan, Mihaylova, Lyudmila, Gning, Amadou, Angelova, Donka, Kadirkamanathan, Visakan

“…Autonomous systems such as Unmanned Aerial Vehicles (UAVs) need to be able to recognise and track crowds of people, e.g. for rescuing and surveillance…”
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