Thermal radiation emitted from objects within a scene tends to be partially
polarised in a direction parallel to the surface normal, to an extent
governed by properties of the surface material. This thesis investigates
whether vehicle detection algorithms can be improved by the additional
measurement of polarisation state as well as intensity in the long wave
infrared.
Knowledge about the polarimetric properties of scenes guides the development
of histogram based and cluster based descriptors which are used
in a traditional classification framework. The best performing histogram
based method, the Polarimetric Histogram, which forms a descriptor
based on the polarimetric vehicle signature is shown to outperform the
standard Histogram of Oriented Gradients descriptor which uses intensity
imagery alone. These descriptors then lead to a novel clustering
algorithm which, at a false positive rate of 10−2 is shown to improve
upon the Polarimetric Histogram descriptor, increasing the true positive
rate from 0.19 to 0.63.
In addition, a multi-modal detection framework which combines thermal
intensity hotspot and polarimetric hotspot detections with a local motion
detector is presented. Through the combination of these detectors, the
false positive rate is shown to be reduced when compared to the result
of individual detectors in isolation
