The system, which incorporates machine learning techniques, is ‘trained’ to identify a range of visual signatures, classifying them based on common characteristics. The training process involves presenting the machine with hundreds of images of a subject to analyse at a variety of angles, distances, and with different obstructions. Over time, this allows it to build up an understanding of how particular people, vehicles, or items should appear.
Still in its development stages, the system can already detect and classify humans and six different types of vehicles, including pick-up trucks, 4x4s and people carriers.
The technology could have applications in a variety of industries, with the security, surveillance, engineering and construction sectors among those that could benefit most.
Dr Pablo Casaseca, senior lecturer in signal and image processing at UWS, said: “The system we’ve created … significantly enhances detection and classification capabilities on thermal imaging cameras at a long-range. The potential applications for the technology could be widespread and if we present the system with enough high quality data, it could detect a whole host of objects with a very small number of pixels.”
CENSIS, the Scottish Innovation Centre for sensor and imaging systems, brokered the relationship between UWS and Thales. CENSIS project manager Gavin Burrows said: “This is a great example of what can be achieved when the right academic institution is paired with industry specialists. Thanks to their partnership, Thales and UWS have created a technology that could revolutionise detection and classification capabilities in a range of industries, placing them at the forefront of research and industrial applications in this area.”
Dr Matt Kitchin, algorithms engineer at Thales in Glasgow, said: “The outcome of phase one has been encouraging – the classification results have yielded very high success rates over a range of imagery.”
