Centrog Feature technique for vehicle type recognition at day and night times

TL;DR

This paper introduces a feature-based vehicle recognition method using CENTROG and CENTRIST features with SVM classifiers, effective for day and night conditions including thermal images at night.

Contribution

It presents a novel application of CENTROG features for vehicle recognition, especially effective in thermal night images, outperforming CENTRIST in accuracy.

Findings

CENTROG achieves higher recognition accuracy than CENTRIST.

Thermal images, despite low resolution, are effectively used for night vehicle recognition.

The method is robust across day and night conditions.

Abstract

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.