Implementation of Random Forest for Vehicle Type Classification using Gamma Correction Algorithm

Abstract

Traffic control systems can be a valuable tool for monitoring road traffic by counting and tracking vehicles in real time. This research is the initial research into the development of methods to improve the accuracy of vehicle detection and recognition. The purpose of the study was to analyze the performance of gamma correction and random forest performance to improve the accuracy of vehicle detection and recognition. Performance measures used in the study were confusion matrix, accuracy, precision, recall, F1-score. Based on experimental results, random forest with gamma=1.5 got the best accuracy of 85.00%, while random forest with gamma=0.5 got accuracy of 81.30%, random forest with gamma=1.0 got accuracy of 84.00%, random forest with gamma=2.0 got accuracy of 84.00%.