BTaRoT: Bayesian Tracking and Reasoning over Time
Lead Research Organisation:
University of Sheffield
Department Name: Automatic Control and Systems Eng
Abstract
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Organisations
- University of Sheffield (Lead Research Organisation)
- Fraunhofer Society (Collaboration)
- Linkoping University (Collaboration)
- Selex ES (Collaboration)
- Qinetiq (United Kingdom) (Collaboration)
- Thales Group (Collaboration)
- University of Lille (Collaboration)
- UNIVERSITY OF LEEDS (Collaboration)
- UNIVERSITY OF CAMBRIDGE (Collaboration)
People |
ORCID iD |
Lyudmila Mihaylova (Principal Investigator) |
Publications
De Freitas Allan
(2015)
How Can Subsampling Reduce Complexity in Sequential MCMC Methods and Deal with Big Data in Target Tracking?
in arXiv e-prints
De Freitas A
(2016)
Autonomous crowds tracking with box particle filtering and convolution particle filtering
in Automatica
Rohou S
(2018)
Reliable non-linear state estimation involving time uncertainties
in Automatica
Mihaylova L
(2014)
Overview of Bayesian sequential Monte Carlo methods for group and extended object tracking
in Digital Signal Processing
Georgieva P
(2016)
A Beamformer-Particle Filter Framework for Localization of Correlated EEG Sources.
in IEEE journal of biomedical and health informatics
Kiring A
(2017)
Tracking With Sparse and Correlated Measurements via a Shrinkage-Based Particle Filter
in IEEE Sensors Journal
Aftab W
(2021)
A Learning Gaussian Process Approach for Maneuvering Target Tracking and Smoothing
in IEEE Transactions on Aerospace and Electronic Systems
De Freitas A
(2019)
A Box Particle Filter Method for Tracking Multiple Extended Objects
in IEEE Transactions on Aerospace and Electronic Systems
Schikora M
(2014)
Box-particle probability hypothesis density filtering
in IEEE Transactions on Aerospace and Electronic Systems
Hawes M
(2017)
Bayesian Compressive Sensing Approaches for Direction of Arrival Estimation With Mutual Coupling Effects
in IEEE Transactions on Antennas and Propagation
Description | The first stage of the research was devoted to modelling interactions between entities in a group of objects and designing sequential Monte Carlo filters. For tracking the motion of the group, box particle filters were developed - for extended and group object tracking. More results are developed for behaviour analysis in video. These are useful for autonomous and semi-autonomous systems. The main software developed has been now made available on the ORDA web site of the University of Sheffield, on GitHub and on ResearchGate. We have results with multiple objects tracking, and mow with machine learning methods such as Gaussian Process regression. These are now published in two journal papers. |
Exploitation Route | Our findings can be used in many different ways - research, industry implementations, ICT applications, security, surveillance, transportation systems and others. The new machine learning approaches that we developed are opening new avenues, especially related with the autonomous knowledge extraction from data. The results can be used in surveillance and manufacturing and now we have projects that are a continuation of BTAROT. |
Sectors | Aerospace Defence and Marine Digital/Communication/Information Technologies (including Software) Education Manufacturing including Industrial Biotechology Security and Diplomacy Transport |
Description | Novel methods were developed for groups and extended object tracking. Some of these are applied in surveillance systems and intelligent transportation systems. The methods include learning Gaussian process methods and particle filters both for sparse data and large volumes of data. Some of the findings are currently used in a EU funded project, called SETA. It is for prediction of mobility in cities. The results and developed approaches during the BTaRoT project led to new projects where autonomy is a key aspect, e.g. BTaRoT lead to a new knowledge exchange grant in manufacturing, for automated and autonomous recognition of the size of the developed objects, to a new project on co-botics funded by the Lloyds Foundation and the NSF-EPSRC Shiras project (awarded in 2019). We received funding via a Dstl and USA MOD funded project called SIGNETs, in November 2020. The SIGNETs project is well related with the BTAROT project and extends it by adding uncertainty quantification. |
First Year Of Impact | 2016 |
Sector | Aerospace, Defence and Marine,Construction,Digital/Communication/Information Technologies (including Software),Education,Environment,Manufacturing, including Industrial Biotechology,Transport |
Impact Types | Economic |
Description | EU FP7 ITN Marie Curie |
Amount | € 615,000 (EUR) |
Funding ID | Grant no. 607400, EU ITN Marie Curie project, TRAX (Training network on tRAcking in compleX sensor systems), http://cordis.europa.eu/project/rcn/109279_en.html |
Organisation | European Commission |
Sector | Public |
Country | European Union (EU) |
Start | 09/2013 |
End | 10/2017 |
Description | SEE MORE MAKE MORE: Secondary Electron Energy Measurement Optimisation for Reliable Manufacturing of Key Materials |
Amount | £1,171,727 (GBP) |
Funding ID | EP/V012126/1 |
Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
Sector | Public |
Country | United Kingdom |
Start | 07/2021 |
End | 01/2025 |
Description | SETA: An open, sustainable, ubiquitous data and service for efficient, effective, safe, resilient mobility in metropolitan areas |
Amount | £339,500 (GBP) |
Funding ID | 688082 |
Organisation | European Commission |
Sector | Public |
Country | European Union (EU) |
Start | 02/2016 |
End | 02/2019 |
Description | SIGNETS:Signal and information Processing for Scalable Decentralised Intelligent Networks |
Amount | $1,000,000 (USD) |
Funding ID | https://www.sheffield.ac.uk/acse/news/signets-advance-fundamental-research-distributed-sensing |
Organisation | University of Sheffield |
Sector | Academic/University |
Country | United Kingdom |
Start | 11/2020 |
End | 11/2023 |
Description | TRAX: Tracking Complex Systems, EU FP7 ITN Marie Curie project |
Amount | € 514,340 (EUR) |
Funding ID | 607400 |
Organisation | European Commission |
Sector | Public |
Country | European Union (EU) |
Start | 09/2013 |
End | 09/2017 |
Description | UK Industry |
Amount | £111,000 (GBP) |
Organisation | Selex ES |
Sector | Private |
Country | Italy |
Start | 08/2013 |
End | 06/2014 |
Description | UKRI Trustworthy Autonomous Systems Node in Resilience |
Amount | £3,063,678 (GBP) |
Funding ID | EP/V026747/1 |
Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
Sector | Public |
Country | United Kingdom |
Start | 11/2020 |
End | 04/2024 |
Title | Box particle and kernel particle methods for object tracking |
Description | We have developed box particle filters and convolutional particle filters for groups and extended object tracking. The data association problem is challenging due to measurement origin uncertainty in such problems. We have proposed efficient ways to deal with this measurement origin uncertainty - in the box particle and Gaussian kernel method - convolutional particle filter. |
Type Of Material | Technology assay or reagent |
Year Produced | 2016 |
Provided To Others? | Yes |
Impact | The developed filters can cope with the measurement origin uncertainty in an elegant way, i.e. resolve the data association problem. For the box particle filter (PF) we derive a theoretical expression of the generalised likelihood function in the presence of clutter. An adaptive convolution particle filter (CPF) is also developed and the performance of the two filters is compared with the standard sequential importance resampling (SIR) PF. The pros and cons of the two filters are illustrated over a realistic scenario (representing a crowd motion in a stadium) for a large crowd of pedestrians. Accurate estimation results are achieved. |
URL | https://www.sciencedirect.com/science/article/pii/S0005109816300887 |
Title | For enhancing mobility in cities |
Description | Vehicular traffic congestion is a significant problem that arises in many cities. This is due to the increasing number of vehicles that are driving on city roads of limited capacity. The vehicular congestion significantly impacts travel distance, travel time, fuel consumption and air pollution. Avoidance of traffic congestion and providing drivers with optimal paths are not trivial tasks. The key contribution of this work consists of the developed approach for dynamic calculation of optimal traffic routes. Two attributes (the average travel speed of the traffic and the roads' length) are utilized by the proposed method to find the optimal paths. The average travel speed values can be obtained from the sensors deployed in smart cities and communicated to vehicles via the Internet of Vehicles and roadside communication units. The performance of the proposed algorithm is compared to three other algorithms: the simulated annealing weighted sum, the simulated annealing technique for order preference by similarity to the ideal solution and the Dijkstra algorithm. |
Type Of Material | Improvements to research infrastructure |
Year Produced | 2016 |
Provided To Others? | Yes |
Impact | This and related works are linked with the collaboration that we established with new partners in the area of smart cities. |
URL | https://www.mdpi.com/1424-8220/16/7/1013/htm |
Title | Particle Methods for groups and extended object tracking |
Description | During the BTARoT project we have created several particle filter methods for tracking and inference for multiple targets, for groups and extended objects. These are methods providing spatio-temporal inference, able to estimate the location and size of objects of interest. Extended object tracking has become an integral part of many autonomous systems during the last two decades. For the first time, this paper presents a generic spatio-temporal Gaussian process (STGP) for tracking an irregular and non-rigid extended object. The complex shape is represented by key points and their parameters are estimated both in space and time. This is achieved by a factorization of the power spectral density function of the STGP covariance function. A new form of the temporal covariance kernel is derived with the theoretical expression of the filter likelihood function. Solutions to both the filtering and the smoothing problems with real and simulated data have been developed. |
Type Of Material | Technology assay or reagent |
Year Produced | 2017 |
Provided To Others? | Yes |
Impact | The methods have been used in surveillance applications. These methods stimulated new partnerships now as part of a trustworthy autonomous systems project. |
URL | https://ieeexplore.ieee.org/document/8601344 |
Title | Algorithms and models for autonomous crowds tracking with box particle filtering and convolution particle filtering |
Description | Models and algorithms for autonomous crowds tracking are developed. The algorithms are: a convolutional particle filter, a box particle filter and a sampling importance resampling (SIR) particle filter. The programs are modular and self-contained. The code implements the algorithms presented in the paper: "Autonomous Crowds Tracking with Box Particle Filtering and Convolution Particle Filtering", Automatica, vol. 69, pp. 380-394, July 2016, http://www.sciencedirect.com/science/article/pii/S0005109816300887 . The Convolutional particle filter and the (SIR) particle filter are implemented and compared with the Box particle filter. The Box particle filter requires Intlab software. |
Type Of Material | Computer model/algorithm |
Year Produced | 2016 |
Provided To Others? | Yes |
Impact | The paper proposes a distributed expectation propagation particle filter able to deal with big data. The data is applicable to object tracking in the presence of a high level of measurement noises including "clutter". The algorithm affords dealing with large volumes of data and to achieve high accuracy. |
URL | http://www.sciencedirect.com/science/article/pii/S0005109816300887 |
Title | Code for "Learning methods for dynamic topic modeling in automated behavior analysis" |
Description | This is source code for the algorithms presented in the paper "Learning Methods for Dynamic Topic Modeling in Automated Behavior Analysis" by Olga Isupova, Danil Kuzin, Lyudmila Mihaylova. Published in IEEE Transactions on Neural Networks and Learning Systems, 2018. DOI: 10.1109/TNNLS.2017.2735364. |
Type Of Material | Computer model/algorithm |
Year Produced | 2017 |
Provided To Others? | Yes |
Impact | The algorithms presented in this paper have lead to new projects in the area of machine learning - for video analytics. |
URL | https://figshare.shef.ac.uk/articles/Code_for_Learning_methods_for_dynamic_topic_modeling_in_automat... |
Title | Compressive Sensing Based Design of Sparse Tripole Arrays (Matlab code) |
Description | This Matlab code generates the results shown in the paper: Hawes, M.; Liu, W.; Mihaylova, L. Compressive Sensing Based Design of Sparse Tripole Arrays. Sensors 2015, 15, 31056-31068. For the code to run you will need to install the freely available package cvx for use with Matlab. This is available from: http://cvxr.com/cvx/download/ |
Type Of Material | Computer model/algorithm |
Year Produced | 2016 |
Provided To Others? | Yes |
Impact | The code presents a solution to the problem of designing sparse linear tripole arrays. In such arrays at each antenna location there are three orthogonal dipoles, allowing full measurement of both the horizontal and vertical components of the received waveform. We formulate this problem from the viewpoint of Compressive Sensing (CS). However, unlike for isotropic array elements (single antenna), we now have three complex valued weight coefficients associated with each potential location (due to the three dipoles), which have to be simultaneously minimised. |
Title | Dynamic Hierarchical Dirichlet Process for Anomaly Detection in Video |
Description | This is a source code and synthetic data for dynamic hierarchical Dirichlet process for anomaly detection in video, introduced in O.Isupova, D.Kuzin, L.Mihaylova "Dynamic Hierarchical Dirichlet Process for Abnormal Behaviour Detection in Video" in Proceedings of 19th International Conference of Information Fusion, Heidelberg, Germany, July 2016. In this approach we consider the problem of anomaly detection as extracting typical motion patterns from data by topic modeling methods and detect video clips as abnormal if they have low values of likelihood computed with respect to these extracted typical motion patterns. Learning and inference is performed in a fully unsupervised manner. |
Type Of Material | Computer model/algorithm |
Year Produced | 2016 |
Provided To Others? | Yes |
Impact | The developed model and algorithms can be used in semi-supervised and unsupervised surveillance systems, including for transportation systems, military and maritime applications and other cases when changes in behaviour needs yo be analysed in an autonomous manner. The code can release the load of human operators and help them in making decisions. |
URL | http://ieeexplore.ieee.org/document/7527962/ |
Title | MATLAB Implementation of a Box Particle Filter for tracking multiple extended objects |
Description | This MATLAB Implementation of a Box Particle Filter is for tracking multiple extended objects. It contains the models, the filter, simulated data and code about the filter performance evaluation. |
Type Of Material | Computer model/algorithm |
Year Produced | 2019 |
Provided To Others? | Yes |
Impact | The filter has been used by researchers to develop other generations of box filters, including random finite sets box particle filters. |
URL | https://figshare.shef.ac.uk/articles/MATLAB_Implementation_of_a_Box_Particle_Filter_for_tracking_mul... |
Title | Matlab Codes for the Shrinkage based Particle Filter |
Description | This is the code for the shrinkage particle filter and the data used forits validation. |
Type Of Material | Computer model/algorithm |
Year Produced | 2017 |
Provided To Others? | Yes |
Impact | The code provides a solution for dealing with sparse data. It is linked with this publication: https://ieeexplore.ieee.org/document/7888926/ The doi is: https://doi.org/10.15131/shef.data.6127049.v1 |
URL | https://figshare.com/articles/Matlab_Codes_for_the_Shrinkage_based_Particle_Filter/6127049/1 |
Title | Signal Model for Location and Orientation Optimisation for Spatially Stretched Tripole Arrays Based on Compressive Sensing (Matlab code) |
Description | This is the model developed for the design of the spatially stretched arrays. The related paper is: M. Hawes, L. Mihaylova, W. Liu, Location and Optimisation Orientation for Spatially Stretched Tripole Arrays Based on Compressed Sensing, IEEE Transactions on Signal Processing, 2017, doi: 10.1109/TSP.2017.2655479 http://ieeexplore.ieee.org/document/7827011/ |
Type Of Material | Computer model/algorithm |
Provided To Others? | No |
Impact | The model can be used in the design of antenna arrays. |
URL | https://search.datacite.org/data-centers/bl.shef |
Title | Signal Model for the paper "Bayesian Compressive Sensing Approaches for Direction of Arrival Estimation with Mutual Coupling Effects (Matlab code) |
Description | This is a model for direction arrival estimation in sensor arrays, with mutual coupling effects. The related paper is M. Hawes, L. Mihaylova, F. Septier, S. Godsill, Bayesian Compressive Sensing Approaches for Direction of Arrival Estimation with Mutual Coupling Effects, IEEE Transactions on Antennas and Propagation, Vol. 65, No. 3, 2017. |
Type Of Material | Computer model/algorithm |
Provided To Others? | No |
Impact | The model can be used in the design of antenna arrays, and by accounting for the mutual coupling effects between the separate elements. |
URL | https://doi.org/10.15131/SHEF.DATA.4535825.V1 |
Description | Fraunhofer Institute (FKIE): collaboration via two ITN Marie Curie projects, MC IMPULSE and TRAX |
Organisation | Fraunhofer Society |
Department | Fraunhofer Institute FKIE |
Country | Germany |
Sector | Academic/University |
PI Contribution | Joint research collaboration in the areas of sensor data fusion and multiple target tracking. Web link to the current EU ITN Marie Curie project, TRAX: http://cordis.europa.eu/project/rcn/109279_en.html The web link to the completed EU ITN Marie Curie project, called MC IMPULSE: https://mcimpulse.isy.liu.se/ |
Collaborator Contribution | Developments of methods for sensor data fusion and multiple target tracking. |
Impact | Joint publications as listed below: * M. Schikora, A. Gning, L. Mihaylova, D. Cremers, W. Koch, Box-Particle Hypothesis Density Filter for Multi-Target Tracking, IEEE Transactions on Aerospace and Electronic Systems, Vol. 50, No. 3, July, 2014, DOI:10.1109/TAES.2014.120238, in print. * N. Petrov, M. Ulmke, L. Mihaylova, A. Gning, M. Schikora, M. Wieneke and W. Koch, On the Performance of the Box Particle Filter for Extended Object Tracking Using Laser Data, Proc. from the IEEE Sensor Data Fusion Workshop: Trends, Solutions, Applications, Bonn, Germany, 4-6 Sept. 2012, pp. 19 - 24. |
Start Year | 2008 |
Description | Linkoeping University, Sweden |
Organisation | Linkoping University |
Country | Sweden |
Sector | Academic/University |
PI Contribution | Joint research in the area of signal processing and novel algorithms for filtering and decision making. |
Collaborator Contribution | Knowledge exchange |
Impact | The outcomes from this collaboration related with the two ITN Marie Curie projects (MC IMPULSE that funished) and the current ITN Marie Curie project, called TRAX are in multidisciplinary areas - engineering, ICT, applied mathematics and have applications to intelligent transportation systems, surveillance and other areas. |
Start Year | 2013 |
Description | QinetiQ |
Organisation | Qinetiq |
Country | United Kingdom |
Sector | Private |
PI Contribution | Results in the areas of sensor data fusion, detection, tracking, reasoning over sensor networks and scene understanding. |
Collaborator Contribution | Knowledge exchange, real testing examples and data |
Impact | Publications * H. Bhaskar, L. Mihaylova, S. Maskell, Automatic Human Body Parts Detection Based on Cluster Background Subtraction and Foreground Learning, Neurocomputing. Special Issue on Behaviours in Video, Vol. 100, No. 1, pp. 58-72, 2013. * A. Gning, L. Mihaylova, S. Maskell, S. K. Pang, S. Godsill, Ground Target Group Structure and State Estimation with Particle Filtering, Proc. of the 11th International Conf. on Information Fusion, Cologne, Germany, pp. 1176 -- 1183, 30 June - 3 July 2008. * A. Gning, L. Mihaylova, S. Maskell, S. K. Pang, S. Godsill, Group Object Structure and State Estimation with Evolving Networks and Monte Carlo Methods, IEEE Transactions on Signal Processing, Vol. 59, No. 4, April, 1383 -- 1396, 2011. |
Start Year | 2006 |
Description | Selex ES, UK |
Organisation | Selex ES |
Country | Italy |
Sector | Private |
PI Contribution | Development of approaches for tracking large groups and related software. |
Collaborator Contribution | Provision of practical scenarios and testing examples. |
Impact | * N. Petrov, L. Mihaylova, A. de Freitas, Crowd Tracking with Box Particle Filtering, Proc. of the International Conf. on Information Fusion, Spain, 2014 * N. Petrov, L. Mihaylova, A. Gning, Rectangular Extended Object Tracking with Box Particle Filter Using Dynamic Constraints, Proceedings of the IET Target Tracking and Data Fusion Conference, Liverpool, UK, 30 April, 2014. |
Start Year | 2011 |
Description | Thales (NL) via two EU Initial Training Network Projects, MC Impulse, and now a second project, called TRAX |
Organisation | Thales Group |
Country | France |
Sector | Private |
PI Contribution | We developed novel algorithms for nonlinear estimation, with various applications such as intelligent transportation systems and surveillance. |
Collaborator Contribution | Provision of data, practical testing examples and scenarios, knowledge exchange. Web link to the current EU ITN Marie Curie project, TRAX (Training network on tRAcking in compleX sensor systems): http://cordis.europa.eu/project/rcn/109279_en.html Web link to the completed EU ITN Marie Curie project, MC IMPULSE https://mcimpulse.isy.liu.se/ |
Impact | This is a multi-disciplinary collaboration, in the areas of engineering, mathematics, signal processing and ICT. |
Start Year | 2013 |
Description | University of Cambridge |
Organisation | University of Cambridge |
Country | United Kingdom |
Sector | Academic/University |
PI Contribution | Development of novel sequential Monte Carlo filtering approaches. Joint publications: * L. Mihaylova, A. Carmi, F. Septier, A. Gning, S. K. Pang, S. Godsill, Overview of Sequential Bayesian Monte Carlo Methods for Group and Extended Object Tracking, Digital Signal Processing, February, 2014, Vol. 25, pp. 1-16. * A. Y. Carmi, L. Mihaylova, S. J. Godsill (Editors), Compressed Sensing and Sparse Filtering}, Series Signals and Communication Technology, Springer, Berlin Heidelberg, ISBN 978-3-642-38397-7, 2014. * A. Carmi, L. Mihaylova, A. Gning, P. Gurfil, S. Godsill, Inferring Leadership from Group Dynamics Using Markov Chain Monte Carlo Methods, Chapter in Modeling, Simulation, and Visual Analysis of Crowds}, Eds. S. Ali, K. Nishino, D. Monacha, M. Sha, Springer, pp. 335-357, 2014. * A. Carmi, L. Mihaylova, A. Gning, P. Gurfil, S. Godsill, Markov Chain Monte Carlo Based Autonomous Tracking and Causality Reasoning, Chapter in Advances in Intelligent Signal Processing and Data Mining: Theory and Applications, Eds. P. Georgieva, L. Mihaylova and L. Jain, pp. 7-53, 2013. * A. Gning, L. Mihaylova, S. Maskell, S. K. Pang, S. Godsill, Group Object Structure and State Estimation with Evolving Networks and Monte Carlo Methods, IEEE Transactions on Signal Processing}, Vol. 59, No. 4, April, 1383 - 1396, 2011. * A. Carmi, L. Mihaylova, F. Septier, S. Pang, S. Godsill, MCMC-Based Tracking and Identification of Leaders in Groups, Proceedings of the First IEEE Workshop on Modeling, Simulation and Visual Analysis of Large Crowds, Barcelona, Spain, 6 - 13 November 2011. * A. Gning, L. Mihaylova, S. Maskell, S. K. Pang, S. Godsill, Ground Target Group Structure and State Estimation with Particle Filtering, Proc. of the 11th International Conf. on Information Fusion}, Cologne, Germany, pp. 1176 - 1183, 30 June - 3 July 2008. * A. Gning, L. Mihaylova, S. Maskell, S. K. Pang, S. Godsill, Evolving Networks for Group Object Motion Estimation, Proc. of the Institution of Engineering and Technology (IET) Seminar on Target Tracking and Data Fusion: Algorithms and Applications}, 15-16 April 2008, Birmingham, UK, pp. 99-106. |
Collaborator Contribution | Joint development of methods for nonlinear filtering, including knowledge exchange. |
Impact | The outcomes are in multidisciplinary areas of engineering, ICT and mathematics. |
Start Year | 2007 |
Description | University of Leeds |
Organisation | University of Leeds |
Country | United Kingdom |
Sector | Academic/University |
PI Contribution | We developed approaches for routing in cities - they led to new projects. |
Collaborator Contribution | My partners contributed with their knowledge in the area of communication systems. |
Impact | four joint journal papers |
Start Year | 2014 |
Description | University of Lille, Telecomme-Lille, France |
Organisation | University of Lille |
Country | France |
Sector | Academic/University |
PI Contribution | Development of methodological works on Markov Chain Monte Carlo (MCMC) methods |
Collaborator Contribution | Francois Septier - bringing knowledge on MCMC methods |
Impact | * L. Mihaylova, A. Carmi, F. Septier, A. Gning, S. K. Pang, S. Godsill, Overview of Sequential Bayesian Monte Carlo Methods for Group and Extended Object Tracking, Digital Signal Processing, February, 2014, Vol. 25, pp. 1-16. * A. Carmi, L. Mihaylova, F. Septier, S. Pang, S. Godsill, MCMC-Based Tracking and Identification of Leaders in Groups, Proceedings of the First IEEE Workshop on Modeling, Simulation and Visual Analysis of Large Crowds, Barcelona, Spain, 6 - 13 November 2011. |
Start Year | 2007 |
Title | Bayesian Compressive Sensing Approaches for Direction of Arrival Estimation with Mutual Coupling Effects |
Description | Matlab code for the signal model used in: M. Hawes, L. Mihaylova, F. Septier and S. Godsill, "Bayesian Compressive Sensing Approaches for Direction of Arrival Estimation with Mutual Coupling Effects" IEEE Transactions on Antennas and Propagation (Volume: 65, Issue: 3, March 2017) https://doi.org/10.1109/TAP.2017.2655013 |
Type Of Technology | Software |
Year Produced | 2017 |
Open Source License? | Yes |
Impact | Code for the design of sparse sensor arrays. |
URL | https://figshare.shef.ac.uk/articles/Signal_Model_for_the_paper_Bayesian_Compressive_Sensing_Approac... |
Title | Compressive Sensing Based Design of Sparse Tripole Arrays (Matlab code) |
Description | This Matlab code generates the results shown in the paper: Hawes, M.; Liu, W.; Mihaylova, L. Compressive Sensing Based Design of Sparse Tripole Arrays. Sensors 2015, 15, 31056-31068. For the code to run you will need to install the freely available package cvx for use with matlab. This is available from: http://cvxr.com/cvx/download/ |
Type Of Technology | Software |
Year Produced | 2016 |
Open Source License? | Yes |
Impact | The code has been used for sparse arrays design. |
URL | https://figshare.shef.ac.uk/articles/Compressive_Sensing_Based_Design_of_Sparse_Tripole_Arrays_Matla... |
Title | Crowd Tracking with the Box Particle Filter |
Description | The software package is for tracking crowds of people. It can be used for surveillance or other purposes. The algorithms developed are general and can be applied in other areas. |
Type Of Technology | Software |
Year Produced | 2016 |
Open Source License? | Yes |
Impact | This is a MATLAB software package implementing the Box particle filter and the convolution particle filter published in the Automatica, 2016 paper. |
URL | http://www.mathworks.com/matlabcentral/fileexchange/55657-crowd-tracking-with-the-box-particle-filte... |
Title | Dynamic Hierarchical Dirichlet Process for Anomaly Detection in Video |
Description | This is a source code and synthetic data for dynamic hierarchical Dirichlet process for anomaly detection in video, introduced in O.Isupova, D.Kuzin, L.Mihaylova "Dynamic Hierarchical Dirichlet Process for Abnormal Behaviour Detection in Video" in Proceedings of 19th International Conference of Information Fusion, Heidelberg, Germany, July 2016. In this approach we consider the problem of anomaly detection as extracting typical motion patterns from data by topic modeling methods and detect video clips as abnormal if they have low values of likelihood computed with respect to these extracted typical motion patterns. Learning and inference is performed in a fully unsupervised manner. If you use this code or model please cite the above mentioned paper. |
Type Of Technology | Software |
Year Produced | 2016 |
Open Source License? | Yes |
Impact | This is the implementation of algorithms for anomaly detection that has been used in autonomous and semi-autonomous systems. |
URL | https://figshare.shef.ac.uk/articles/Dynamic_Hierarchical_Dirichlet_Process_for_Anomaly_Detection_in... |
Title | Learning Methods for Dynamic Topic Modeling in Automated Behavior Analysis - Code and Implementation |
Description | This is source code for the algorithms presented in the paper "Learning Methods for Dynamic Topic Modeling in Automated Behavior Analysis" by Olga Isupova, Danil Kuzin, Lyudmila Mihaylova. Published in IEEE Transactions on Neural Networks and Learning Systems, 2017. DOI: 10.1109/TNNLS.2017.2735364. Two learning methods for the Markov Clustering Topic Model (MCTM) are developed - Expectation-Maximisation (EM) algorithm and Variational Bayes (VB) inference. Implementation is done in Matlab. |
Type Of Technology | Software |
Year Produced | 2018 |
Open Source License? | Yes |
Impact | The software led to the development of new behaviour analysies approaches. |
URL | https://figshare.shef.ac.uk/articles/Code_for_Learning_methods_for_dynamic_topic_modeling_in_automat... |
Title | MATLAB Implementation of a Box Particle Filter for tracking multiple extended objects |
Description | This is a software MATLAB Implementation of a Box Particle Filter for tracking multiple extended objects for tracking multiple extended objects. The algorithm is a Box particle filter. The program is modular and self-contained. The code is linked with the paper: "A Box Particle Filter Method for Tracking Multiple Extended Objects", doi: 10.1109/TAES.2018.2874147. The Box particle filter requires a library called Intlab for operation. |
Type Of Technology | Software |
Year Produced | 2019 |
Open Source License? | Yes |
Impact | The developed approach raised interest in ONERA and their navigation platforms. |
URL | https://figshare.shef.ac.uk/articles/MATLAB_Implementation_of_a_Box_Particle_Filter_for_tracking_mul... |
Title | Matlab Codes for the Shrinkage Particle Filter Dealing with Sparse Data |
Description | Matlab Codes for the Shrinkage based Particle Filter Fileset posted on 25.04.2018, 17:12 by Aroland Kiring Lyudmila Mihaylova Jose Esnaola It contains the Matlab codes for the developed shrinkage based particle filter. The shrinkage based particle filter combined the shrinkage estimator and the particle filter to jointly estimate the shadowing noise covariance matrix of measurements and the state of the mobile user. |
Type Of Technology | Software |
Year Produced | 2017 |
Open Source License? | Yes |
Impact | The software is useful to estimate the unknown measurement covariance matrix, for tracking and localisation tasks. |
URL | https://figshare.shef.ac.uk/articles/Matlab_Codes_for_the_Shrinkage_based_Particle_Filter/6127049/1 |
Description | A talk on: "UKRI Uncertainty-Aware Machine Learning Methods for Trustworthy Autonomous Systems" |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Professional Practitioners |
Results and Impact | A talk at the "Trusting Machines? Cross-sector Lessons from Healthcare and Security" event The event was created as part of the collaboration between RUSI, the TAS Hub and National Gallery X. The topic of the toc is summarised briefly below: Sensors provide enormous amounts of information beyond the capacity that a human could process. Being part of autonomous systems, the data are coming in real time and need to be processed quickly and fed to the control and decision-making levels. This talk will discuss recently developed machine learning methods able to deal with data challenges such as volume, velocity, veracity and variety. The recent trends in machine learning and autonomy are towards development of trustworthy solutions, able to work under different conditions - due to the external environmental changes and the dynamics of the autonomous systems. Autonomous systems need to be safe and reliable, and include both information from hard and soft sensors - cameras, LiDARs, radars, wireless sensor networks and data from the Internet or other sources. Generic principles valid for at least four domains will be discussed - for surveillance, health, manufacturing and transport systems. This talk aims to stimulate discussions from multi-disciplinary areas and consider questions like these: • How could be define different levels of trustworthiness and resilience, respectively? • How could we characterise trustworthiness? What criteria could we have? How could we quantify the impact of uncertainties and provide resilience? • What are going to be the next generations of methods for uncertainty-aware autonomous systems? • How could we fuse reliably the data from multiple heterogeneous sensors, in order to provide resilience? • How do we link the technological aspects with ethics, societal and human-centred factors? |
Year(s) Of Engagement Activity | 2021 |
URL | https://rusi.cplus.live/event/trusting-machines-21 |
Description | Invited talk for the Science and Technology Organisation Specialists Meeting on "Artificial Intelligence for Military Multisensor Fusion Engines NATO-SET-262 organized by the Sensors and Electronics Technology Panel MSE Focus group, Dec. 2018 |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Professional Practitioners |
Results and Impact | This was a NATO workshop which was a very important event - with new contacts created after my talk - with IBM, people from Amazon, US Air forces, surveillance companies and colleagues from Fraunhofer, Germany. Many questions were asked about the extended and group object tracking methods, about the trends in machine learning. |
Year(s) Of Engagement Activity | 2019 |
URL | https://events.sto.nato.int |
Description | Machine Learning Methods for Autonomous Image and Video Analytics - Invited talk |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Other audiences |
Results and Impact | I gave a talk on recent developments on trustworthy machine learning methods for image and video analytics. |
Year(s) Of Engagement Activity | 2021 |
URL | http://www.icdip.org/ |
Description | Towards Trustworthy Machine Learning Methods |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Other audiences |
Results and Impact | This is an invited talk as part of the Terrorism Risk Assessment, Modelling and Mitigation Seminar Series (TRAMMSS) at Cranfield University, 11 November 2022 |
Year(s) Of Engagement Activity | 2022 |
URL | https://www.cranfield.ac.uk/events/events-2022/towards-trustworthy-machine-learning-methods |
Description | Towards Trustworthy Machine Learning Methods |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Other audiences |
Results and Impact | This is an invited plenary talk as part of the 12th International Conference on Electronics, Communications and Networks (CECNet 2022), November 4th-7th, 2022, Online Conference, Beijing. The talk stimulated interesting discussions and possibly new research collaborations. |
Year(s) Of Engagement Activity | 2022 |
URL | http://cecnetconf.org/Program |