Breaches Detection in Zebra Cross Traffic Light Using Haar Cascade Classifier

Abstract

Traffic violations are common in the area zebra crossing at the location of traffic lights, violations are generally caused by the negligence of motorists who do not comply with existing regulations. As a result, there were many traffic accidents that could have been avoided. Starting from this problem, a simulation model tool is needed that is able to provide direct appeals to drivers when a violation occurs. Based on this, the purpose of this research is to create a simulation model to prevent accidents caused by motorists who are negligent of the rules by identifying vehicles that stop crossing the line boundaries. zebracross or drivers waiting for an out of place traffic light using imagery HaarCascade Classifiers which is programmed using Python and OpenCV to help process digital images. The results of this study that the Haar Cascade Classifier algorithm can characterize motorbikes well, with the best average accuracy value of 91.5% at a resolution of 720p. Based on the results obtained shows that the algorithm haar cascade classifier can detect violations that exist at traffic lights. Hence this indicates the rate of traffic violations can reduce by utilizing the algorithm haar cascade classifier.