Open AccessSistem Penegakan Speed Bump Berdasarkan Kecepatan Kendaraan yang Diklasifikasikan Haar Cascade Classifier
Author(s) -
Muhammad Zulfikri,
Erni Yudaningtyas,
Rahmadwati Rahmadwati
Publication year - 2019
Publication title -
jurnal teknologi dan sistem komputer
Language(s) - English
Resource type - Journals
eISSN - 2620-4002
pISSN - 2338-0403
DOI - 10.14710/jtsiskom.7.1.2019.12-18
Subject(s) - haar like features , adaboost , cascading classifiers , cascade , artificial intelligence , python (programming language) , computer science , haar , speed limit , classifier (uml) , computer vision , pattern recognition (psychology) , real time computing , engineering , transport engineering , face detection , operating system , random subspace method , chemical engineering , wavelet , facial recognition system
Driving at high speed is among the frequent causes of accidents. In this research, a warning system was developed to warn drivers when their speed beyond the safety limit. Haar cascade classifier was proposed for the detection system which comprises Haar features, integral image, AdaBoost learning, and cascade classifier. The system was implemented using Python OpenCV library and evaluated on road traffic video collected in one way traffic. As a result, the proposed method yields 97.92% of car detection accuracy in daylight and MSE of 2.88 in speed measurement.