We use cookies to ensure that we give you the best experience on our website. You can change your cookie settings at any time. Otherwise, we'll assume you're OK to continue.

Durham University

Computer Science


Publication details for Mr Samet Akcay

Kundegorski, M.E., Akcay, S., Payen de La Garanderie, G. & Breckon, T.P. (2016), Real-time Classification of Vehicle Types within Infra-red Imagery, in Burgess, D., Owen, G., Bouma, H., Carlysle-Davies, F., Stokes, R.J. & Yitzhaky, Y. eds, Proceedings of SPIE 9995: Optics and Photonics for Counterterrorism, Crime Fighting and Defence XII. Edinburgh, United Kingdom, SPIE (Society of Photo-optical Instrumentation Engineers), Washington, USA, 99950T.

Author(s) from Durham


Real-time classification of vehicles into sub-category types poses a significant challenge within infra-red imagery due to the high levels of intra-class variation in thermal vehicle signatures caused by aspects of design, current operating duration and ambient thermal conditions. Despite these challenges, infra-red sensing offers significant generalized target object detection advantages in terms of all-weather operation and invariance to visual camouflage techniques. This work investigates the accuracy of a number of real-time object classification approaches for this task within the wider context of an existing initial object detection and tracking framework. Specifically we evaluate the use of traditional feature-driven bag of visual words and histogram of oriented gradient classification approaches against modern convolutional neural network architectures. Furthermore, we use classical photogrammetry, within the context of current target detection and classification techniques, as a means of approximating 3D target position within the scene based on this vehicle type classification. Based on photogrammetric estimation of target position, we then illustrate the use of regular Kalman filter based tracking operating on actual 3D vehicle trajectories. Results are presented using a conventional thermal-band infra-red (IR) sensor arrangement where targets are tracked over a range of evaluation scenarios.