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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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 10/2013 
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 02/2021 
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 10/2013 
End 09/2017
 
Description UK Industry
Amount £111,000 (GBP)
Organisation Selex ES 
Sector Private
Country Italy
Start 09/2013 
End 06/2014
 
Description UKRI Trustworthy Autonomous Systems Node in Resilience
Amount £3,033,177 (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 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
 
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