Automated Visual Surveillance

Durham Computer Science research on computer vision algorithms enables automated image understanding to provide long term wide-area surveillance of dynamic scene objects (e.g. people, vehicles) addressing questions such as: “Is there anything there?” (detection); “What is it?” (classification); “Where is it?” (localization); and “What is it’s behaviour?” (tracking).

This research, as part the SAPIENT programme, informs scientific work by the governments of UK, USA, Canada, Australia, New Zealand and Netherlands on wide-area, multi-sensor surveillance systems. Our research has contributed to £23.2 million investment in multi-sensor surveillance systems (UK/US government/industry), £11.3 million of additional commercial income to UK companies and supported the creation of around 55 additional science and engineering jobs across six organisations.

As of 2022, the open software architecture developed within the SAPIENT programme is now being used by NATO for the evaluation of counter-drone technologies.

UK government paper (guidance): SAPIENT autonomous sensor system

Press release: NATO trials Dstl standard for counter-drone systems - DSTL news story

Press release: Swarming drones concept flies closer to reality - DSTL press release

Find out more

• Learn more about the work of Toby Breckon, Professor in our Department of Computer Science and Department of Engineering,
• Visit Professor Toby Breckon’s website.
• Read related publications:
  • Multi-Modal Learning for Real-Time Automotive Semantic Foggy Scene Understanding via Domain Adaptation (N. Alshammari, S. Akcay, T.P. Breckon), In Proc. Intelligent Vehicles Symposium, IEEE, pp. 1428-1435, 2021.
  • Eliminating the Dreaded Blind Spot: Adapting 3D Object Detection and Monocular Depth Estimation to 360° Panoramic Imagery (G. Payen de La Garanderie, A. Atapour-Abarghouei, T.P. Breckon), In Proc. European Conference on Computer Vision, Springer, pp. 812-830, 2018.
  • From On-Road to Off: Transfer Learning within a Deep Convolutional Neural Network for Segmentation and Classification of Off-Road Scenes (C.J. Holder, T.P. Breckon, X. Wei), In Proc. European Conference on Computer Vision Workshops, Springer, pp. 149-162, 2016
• Interested in studying at Durham? Explore our undergraduate and postgraduate courses in Department of Computer Science.

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