The Security theme includes cyber security, visual surveillance, crowd analysis and network security.
Some examples of security related projects:
This project aims at providing more efficient and secure encryption algorithms for digital imaging. In particular, new modes of operation based on all-or-nothing transforms (AONTs) will be devised to strengthen symmetric block-ciphers against ciphertext attacks.
Dr Eckhard Pfluegel's paper entitled "Chaos-Based Image Encryption Using an AONT" was selected as one of the best paper awards for the International Conference on Cyber Security and Protection of Digital Services (Cyber Security 2015). This paper is the outcome of a successful collaboration between the Networks and Mathematics department of the new School of Computer Science and Mathematics.
PROACTIVE (PRedictive reasOning and multi-source fusion empowering AntiCipation of attacks and Terrorist actions In Urban EnVironmEnts).
The main goal of PROACTIVE is to research a holistic citizen-friendly multi sensor fusion and intelligent reasoning framework enabling the prediction, detection, understanding and efficient response to terrorist interests, goals and courses of actions in an urban environment. To this end, PROACTIVE will rely on the fusion of both static knowledge (i.e. intelligence information) and dynamic information (i.e. data observed from sensors deployed in the urban environment).
Remagnino, Paolo, Velastin, Sergio A., Foresti, Gian Luca and Trivedi, Mohan (2007) Editorial: Novel concepts and challenges for the next generation of video surveillance. Machine Vision and Applications, 18(3-4), pp. 135-137. ISSN (print) 0932-8092
Orwell, J., Remagnino, P.M. and Jones, G.A. (2001) Optimal color quantization for real-time object recognition. Real-Time Imaging, 7(5), pp. 401-414. ISSN (print) 1077-2014
This is a collaboration between human guards, Security network and robotic platforms, US Department of Homeland Security, University Research in Homeland Security Science, a consortium led by the University of Nevada, with Kingston University, Sapienza University of Rome and University of Ulster, Jordanstown.
Espina, Maria Valera et al. 'Multi-Robot Teams For Environmental Monitoring'. Innovations in Defence Support Systems - 3 (2011): 183-209. Web. 29 Sept. 2015
The human brain recognises human actions by not only extracting relevant visual information, but also applying logical reasoning. It is proposed to follow a similar process within a computer vision framework where machine learning-based results are refined by a common-sense reasoning system.
Episode Reasoning for Vision-Based Human Action Recognition, Maria J. Santofimia, J. Martinez del Rincon, and J.-C. Nebel, The Scientific World Journal, vol. 2014, Article ID 270171, 18 pages, 2014
Common Sense Reasoning for Human Action Recognition, J. Martinez del Rincon, Maria J. Santofimia and J.-C. Nebel, Pattern Recognition Letters, 34(15), pp 1849-1860, 2013
Common-Sense Knowledge for a Computer Vision System for Human Action Recognition, M. J. Santofimia, J. Martinez del Rincon, and J.-C. Nebel, International Conference on Ubiquitous Computing and Ambient Intelligence (UCAmI 2012), Vitoria-Gasteiz, Spain, December 3-5, 2012