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Real-time Classification of Vehicle Types within Infra-red Imagery

Kundegorski, M.E.; Akcay, S.; Payen de La Garanderie, G.; Breckon, T.P.; Stokes, R.J.

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Authors

M.E. Kundegorski

Samet Akcay samet.akcay@durham.ac.uk
PGR Student Doctor of Philosophy

G. Payen de La Garanderie

R.J. Stokes



Contributors

D. Burgess
Editor

F. Carlysle-Davies
Editor

G. Owen
Editor

H. Bouma
Editor

R.J. Stokes
Editor

Y. Yitzhaky
Editor

Abstract

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.

Citation

Kundegorski, M., Akcay, S., Payen de La Garanderie, G., Breckon, T., & Stokes, R. (2016). Real-time Classification of Vehicle Types within Infra-red Imagery. In D. Burgess, F. Carlysle-Davies, G. Owen, H. Bouma, R. Stokes, & Y. Yitzhaky (Eds.), Proc. SPIE Optics and Photonics for Counterterrorism, Crime Fighting and Defence (1-16). https://doi.org/10.1117/12.2241106

Conference Name Optics and Photonics for Counterterrorism, Crime Fighting and Defence XII
Conference Location Edinburgh, United Kingdom
Acceptance Date May 30, 2016
Publication Date 2016
Deposit Date Sep 15, 2016
Publicly Available Date Mar 29, 2024
Volume 9995
Pages 1-16
Series Title Proceedings of SPIE
Series ISSN 0277-786X
Book Title Proc. SPIE Optics and Photonics for Counterterrorism, Crime Fighting and Defence
ISBN 9781510603943
DOI https://doi.org/10.1117/12.2241106
Keywords vehicle sub-category classification, thermal target tracking, bag of visual words, histogram of oriented gradient, convolutional neural network, sensor networks, passive target positioning, vehicle localization
Public URL https://durham-repository.worktribe.com/output/1149779
Publisher URL https://breckon.org/toby/publications/papers/kundegorski16vehicle.pdf
Related Public URLs http://community.dur.ac.uk/toby.breckon/publications/papers/kundegorski16vehicle.pdf

Files

Accepted Conference Proceeding (3.4 Mb)
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Copyright 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.






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