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FUSION 2016 PLENARY TALKS


Wednesday, July 6

Fredrik Gustafsson, Linköping University, Linköping, Sweden

Project Ngulia: Tracking Rangers, Rhinos and Poachers

Linköping University started the initiative Wildlife Security in 2013 to support the rhino sanctuary Ngulia in Kenya with sensor fusion technology. Project Ngulia is a pilot project on border security defined by Kenya Wildlife Service, managed by the Stimson Center, Washington DC. The motivation comes from the escalating number of rhinos being poached, and the goal is to develop innovative cost efficient technology to digitise the two main tasks for the rangers: conservation and security. For both these tasks, the enabling technology is localisation of the main actors: rhinos, rangers and poachers. The presentation will describe the current status with a cloud based reporting system in Ngulia and advanced sensor systems being tested at Kolmården Zoo in Sweden. These sensor systems currently include GPS tags on animals, radar, surveillance cameras, microphone arrays, radio arrays and drones.

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Fredrik Gustafsson, Linköping University

Fredrik Gustafsson is professor in Sensor Informatics at the Department of Electrical Engineering, Linköping University, since 2005. He received the MSc degree in electrical engineering 1988 and the Ph.D. degree in Automatic Control, 1992, both from Linköping University. His work in the sensor fusion area involves design and implementation of nonlinear filtering algorithms for localization, navigation and tracking of all kind of platforms, including cars, aircraft, spacecraft, UAV's, surface and underwater vessels, cell phones and animals. He is a co-founder of the companies NIRA Dynamics (automotive safety, including tire pressure monitoring systems found in more than 20 million cars), Softube (plug-ins for music studios and software solutions for Marshall and Fender), and Senionlab (leading manufacturer of indoor navigation solutions for smartphones).

He is an elected member of the Royal Academy of Engineering Sciences (IVA) 2007, and was elevated to IEEE Fellow 2011. His has received paper awards from IEEE AESS Magazine 2010 and Automatica 2012.


Thursday, July 7

Simon Godsill, University of Cambridge, Cambridge, United Kingdom

Incorporation of stochastic behaviour and intent into multiple object dynamical systems

In this talk I will describe recent methods and applications for high-level inference and tracking of multiple object, groups and networks, by incorporation of behavioural interactions and unobserved intent into the dynamical models. The idea here is to extend the standard multiple object tracking paradigm to one in which we may automatically learn dynamic interactions between those objects, as well as infer possible intentionalities of the objects. Our models are based on principles from animal behavioural analysis in which objects follow patterns of behaviour based loosely upon what their neighbours in the group are doing, and upon the (unknown) intentionality of the group, for example its final destination. We may also learn more complex interactions such as whether one member of the group is a `leader' of the dynamics and how the objects are split between different groupings. Models are typically formulated in continuous time, and inference is carried out on-line using numerical Bayesian filtering strategies, implemented with state of the art methods such as particle filters and Markov chain Monte Carlo. Applications will be presented from the areas of vehicle tracking, wild animal pack hunting behaviour analysis, financial time series, and finally applications in User Interfaces for automobiles in which the task is to determine accurately and rapidly the intended icon a user is pointing at on a screen, based on the trajectory of hand motion near to the screen, and in the presence of disturbances from suspension and road surface.


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Simon Godsill is Professor of Statistical Signal Processing in the Engineering Department at Cambridge University. He is also a Professorial Fellow and tutor at Corpus Christi College Cambridge. He coordinates an active research group in Signal Inference and its Applications and is Head of the Signal Processing and Communications Laboratory at Cambridge. His group specialises in Bayesian computational methodology, multiple object tracking, audio and music processing, and financial time series modeling. A particular methodological theme over recent years has been the development of novel techniques for optimal Bayesian filtering and smoothing, using Sequential Monte Carlo or Particle Filtering methods. Prof. Godsill has published extensively in journals, books and international conference proceedings, and has given a number of high profile invited and plenary addresses at conferences such as the Valencia conference on Bayesian Statistics (twice), the IEEE Statistical Signal Processing Workshop, the Conference on Bayesian Inference for Stochastic Processes (BISP), the IEEE Workshop on Machine Learning in Signal Processing (2013) and FUSION (2016). He co-authored a Springer text Digital Audio Restoration with Prof. Peter Rayner in 1998. He was technical chair of the IEEE NSSPW workshop in 2006 on sequential and nonlinear filtering methods, and has been on the conference panel for numerous other conferences/workshops. Prof. Godsill has served as Associate Editor for IEEE Tr. Signal Processing and the journal Bayesian Analysis. He was Theme Leader in Tracking and Reasoning over Time for the UK’s Data and Information Fusion Defence Technology Centre (DIF-DTC) and Principal Investigator on many grants funded by the EU, EPSRC, QinetiQ, General Dynamics, MOD, Microsoft UK, Citibank, Mastercard, Google, DSO Singapore, Huawei and Jaguar Landrover. In 2009-10 he was co-organiser of an 18 month research program in Sequential Monte Carlo Methods at the SAMSI Institute in North Carolina and in 2014 he co-organised a research programme at the Isaac Newton Institute on Sequential Monte Carlo methods. In 2018 he will be General Chair of the FUSION Conference in Cambridge. Two of his journal papers have recently received Best Paper awards from the IEEE and IET. He continues to be a Director of CEDAR Audio Ltd. (which has received numerous accolades over the years, including a technical Oscar), and for which he was a founding staff member in 1988. The company has commercialised many of the ideas from Professor Godsill’s research over the years.


Friday, July 8

Oliver Pink, Robert Bosch GmbH, Abstatt, Germany

Challenges on the Way to Automated Driving

Automated driving has become one of the major trends in automotive industry. While today's advanced driver assistance systems already help drivers reach their destinations safely and more comfortably, automated vehicles will be able to brake, accelerate, and steer the vehicle without permanent driver supervision.
The increase in safety and functional requirements when transitioning from driver assistance to automated driving has great impact on the vehicle architecture, subsystems and vehicle components.

As a component and system supplier, Bosch is actively developing solutions, such as a suitable surround sensor set, braking and steering systems, high-performance ECUs, communication infrastructure, and the development of suitable algorithms. Bosch is testing these solutions in prototype vehicles on public roads in Germany and the United States.

This talk will outline the challenges that we see on the way to automated driving, with a special focus on surround sensing and sensor fusion. Based on a set of particularly challenging use cases, key constraints for the choice of a suitable sensor set will be shown. Algorithmic approaches for fusing multiple sensors of different modalities will be discussed. The talk will further give an overview of using surround sensors for vehicle localization and map building.

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Oliver Pink, Robert Bosch GmbH

Oliver Pink is senior expert in the field of automated driving in the Chassis Systems Control division of Bosch in Abstatt, Germany. His current field of activity includes sensor fusion and environment perception for automated driving. He holds a Ph.D. in Mechanical Engineering and an M.Sc. in Electrical Engineering from the Karlsruhe Institute of Technology. His thesis topic was camera based vehicle localization. He has been active in several research areas related to automated driving since the 2007 DARPA Urban Challenge, where he participated as member of Team AnnieWay.


Friday, July 8

Ba Tuong Vo, Curtin University, Perth, Australia

Beyond the PHD Filter: Advances in Random Set Approaches

Multi-object systems are complex dynamical systems in which the number of objects and their states are unknown and vary randomly with time. Multi-object systems arise in many application areas, including defence, computer vision, field robotics, biomedical research and machine learning. Indeed multi-object systems are ubiquitous in nature. Pioneered by Mahler almost two decades ago, the introduction of the random set or Finite Set Statistics approach to estimation and control for multi-object systems has attracted substantial interest from academia and industry alike. Though the Probability Hypothesis Density (PHD) filter has since become synonymous with the random set paradigm, the last decade has witnessed many new exciting developments beyond the PHD filter. This seminar presents an overview of the random set approach to multi-object systems and outlines recent theoretical and computational developments as well as new applications such as sensor control, visual surveillance, simultaneous localization and mapping, and autonomous driving.


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Ba Tuong Vo is currently an Associate Professor with the Department of Electrical and Computer Engineering at Curtin University in Perth, Australia. He obtained Bachelor degrees in Science majoring in Applied Mathematics, Engineering majoring in Electrical and Electronic Engineering, and PhD with distinction all from The University of Western Australia. He has active research interests in multi-target tracking, Bayesian estimation, and statistical signal processing, and is currently an associate editor for IEEE Signal Processing Letters. He is also a recipient of an Australian Research Council Fellowship and a co recipient the 2010 DSTO Australian Museum Eureka Prize for "Outstanding Contributions in Support of Defense or National Security". He is best known for his works on random finite set algorithms for multi-sensor multi-target tracking.


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