CENTROG FEATURE TECHNIQUE FOR VEHICLE TYPE RECOGNITION AT DAY AND NIGHT TIMES
Martins E. Irhebhude, Philip O. Odion and Darius T. Chinyio
Faculty of Science, Department of Computer Science, Nigerian Defence Academy,
Kaduna, Nigeria.
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.
KEYWORDS
CENTROG, CENTRIST, Vehicle Type Recognition, Day-time Recognition, Night-time Recognition, Classification
Original Source Link : http://aircconline.com/ijaia/V7N6/7616ijaia04.pdf
Artificial Intelligence & Applications - Research 2017-2018 