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.

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, November). Real-time Classification of Vehicle Types within Infra-red Imagery. Presented at Optics and Photonics for Counterterrorism, Crime Fighting and Defence XII, Edinburgh, United Kingdom

• Presentation Conference Type: Conference Paper (published)
• Conference Name: Optics and Photonics for Counterterrorism, Crime Fighting and Defence XII
• Acceptance Date: May 30, 2016
• Publication Date: 2016
• Deposit Date: Sep 15, 2016
• Publicly Available Date: Jun 21, 2017
• 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