Vehicle Classification using Haar Cascade Classifier Method in Traffic Surveillance System

Vehicle Classification using Haar Cascade Classifier Method in Traffic Surveillance System

Moch Ilham Ramadhani, Agus Eko Minarno, Eko Budi Cahyono


Object detection based on digital image processing on vehicles is very important for establishing monitoring system or as alternative method to collect statistic data to make efficient traffic engineering decision. A vehicle counter program based on traffic video feed for specific type of vehicle using Haar Cascade Classifier was made as the output of this research. Firstly, Haar-like feature was used to present visual shape of vehicle, and AdaBoost machine learning algorithm was also employed to make a strong classifier by combining specific classifier into a cascade filter to quickly remove background regions of an image. At the testing section, the output was tested over 8 realistic video data and achieved high accuracy. The result was set 1 as the biggest value for recall and precision, 0.986 as the average value for recall and 0.978 as the average value for precision.


Haar-like feature, AdaBoost, Cascade classifier, Vehicle Detection, Digital Image Processing

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