Long-wave infrared polarimetric cluster-based vehicle detection

Christopher Dickson, Andrew M. Wallace, Matthew Kitchin, Barry Connor

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)


The sensory perception of other vehicles in cluttered environments is an essential component of situational awareness for a mobile vehicle. However, vehicle detection is normally applied to visible imagery sequences, while in this paper we investigate how polarized, infrared imagery can add additional discriminatory power. Using knowledge about the properties of the objects of interest and the scene environment, we have developed a polarimetric cluster-based descriptor to detect vehicles using long-wave infrared radiation in the range of 8–12 μm. Our approach outperforms both intensity and polarimetric image histogram descriptors applied to the infrared data. For example, at a false positive rate of 0.01 per detection window, our cluster approach results in a true positive rate of 0.63 compared to a rate of 0.05 for a histogram of gradient descriptor trained and tested on the same dataset. In conclusion, we discuss the potential of this new approach in comparison with state-of-the-art infrared and conventional video detection.
Original languageEnglish
Pages (from-to)2307-2315
Number of pages9
JournalJournal of the Optical Society of America A
Issue number12
Early online date10 Nov 2015
Publication statusPublished - 1 Dec 2015


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