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Expertise in computer vision has been developed across two decades with applications targeting broadly visual surveillance and medical imaging. Many recent projects have focused on machine learning-based computer vision, including deep learning, in domains such as visual analytics in the context of security in public spaces, processing of images captured by unmanned aerial vehicles, and quantitative medical imaging to aid diagnostics.
To support these activities, Kingston University invested in a 24 GPU-farm based on NVIDIA Titan and a High-Performance Computing facility with 768 cores (Xeon® Scalable Processors at 2.1 GHz, 8GB memory per CPU Core), a 10 Gbps Ethernet) and a useable storage capacity of 80TB.
Intelligent computer vision improving security in crowded public events and agricultural practice on farms (Remagnino). This research has improved the welfare and economic prosperity of citizens across Europe (see Impact Case Study).
The design of a novel end-to-end 3D face reconstruction approach from a single 2D facial image based on a new CNN architecture (Argyriou). In 2017, it was presented at the prestigious International Conference on Computer Vision (ICCV).
The automation of retinal vessels morphology quantification (S. Barman). This was applied to the UK Biobank fundus image dataset (over 100,000 retinal images) and contributed to diabetic retinopathy as evidenced by a publication in the journal Computerized Medical Imaging & Graphics (2015).
Research on modelling sets of multivariate sequences using nonlinear dimensionality reduction delivered a new approach addressing stylistic variations in time series (Makris and Nebel). Published in the journal IEEE Transactions on Cybernetics (2014), this work has applications beyond human motion analysis such as autonomous transport and other self-organising areas.
Research into computer vision and visual surveillance conducted by Remagnino and ROVIT have improved the welfare and economic prosperity of citizens across Europe by:
Research at Kingston University into methods for tracking pedestrians and monitoring crowds using computer vision techniques has been translated into commercial products by Ipsotek Ltd and BAe Systems, resulting in economic benefits to these companies from sales of these products.
These products have been sold to high-profile customers including the London Eye, the O2 Arena and the Australian Government, providing significant commercial benefits, employment and growth for both companies, as well as providing an economic impact for these customers.
Research at Kingston University into methods for tracking sports participants in an arena have been translated into a BAFTA-award-winning system deployed by Channel 4 at the London Paralympics: a "multi-platform Optical Tracking solution for Wheelchair Rugby & Basketball, capable of detecting live impact speeds".
This system was deployed at the London O2 Arena and the Olympic Basketball arena, to provide real-time analysis of player speeds, cumulative distances, impact magnitudes, and other quantitative statistics. There are plans to extend and improve this technology for subsequent events.
This system had economic benefits for the commercial partner, DeltaTre Ltd, and social benefits in contributing to Channel 4's positive portrayal of disabled athletes.