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Real-time classification of vehicle types within infra-red imagery.

Kundegorski, M.E. and Akcay, S. and Payen de La Garanderie, G. and Breckon, T.P. (2016) 'Real-time classification of vehicle types within infra-red imagery.', in Optics and photonics for counterterrorism, crime fighting, and defence XII. Washington, USA: SPIE (Society of Photo-optical Instrumentation Engineers), 99950T. Proceedings of SPIE., 9995


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.

Item Type:Book chapter
Full text:(AM) Accepted Manuscript
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Publisher statement:Copyright 2016. Society of Photo Optical Instrumentation Engineers (SPIE). One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this publication for a fee or for commercial purposes, or modification of the contents of the publication are prohibited.
Date accepted:30 May 2016
Date deposited:21 June 2017
Date of first online publication:November 2016
Date first made open access:No date available

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