  This work proposes a feature-based technique to recognize vehicle types
within day and night times. Support vector machine (SVM) classifier is applied
on image histogram and CENsus Transformed histogRam Oriented Gradient (CENTROG)
features in order to classify vehicle types during the day and night. Thermal
images were used for the night time experiments. Although thermal images suffer
from low image resolution, lack of colour and poor texture information, they
offer the advantage of being unaffected by high intensity light sources such as
vehicle headlights which tend to render normal images unsuitable for night time
image capturing and subsequent analysis. Since contour is useful in shape based
categorisation and the most distinctive feature within thermal images, CENTROG
is used to capture this feature information and is used within the experiments.
The experimental results so obtained were compared with those obtained by
employing the CENsus TRansformed hISTogram (CENTRIST). Experimental results
revealed that CENTROG offers better recognition accuracies for both day and
night times vehicle types recognition.
