Open AccessDetection of level crossing barriers using the histogram of oriented gradients method and support vector machine
Author(s) -
Ahmad Sugiana,
Bandiyah Sri Aprillia,
Muhamad Naufan Rifqi
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/830/3/032043
Subject(s) - level crossing , support vector machine , histogram , train , histogram of oriented gradients , computer science , artificial intelligence , computer vision , classifier (uml) , pattern recognition (psychology) , transport engineering , engineering , geography , cartography , image (mathematics) , mechanical engineering
Railroad crossing is a place where the railroad lines intersect with other roads, such as a highway. Referring to the Regulation of Minister of Transportation No. 36/2011, level crossing must be equipped with signs, markers and traffic signaling devices and crossing gate guards. However, 4600 of the 5800 level crossing points are without railroad keeper so that they are prone to traffic accidents. In addition, hazard information (danger signs) from the railroad keeper to the PUSDALOP and machinists sometimes cannot be seen at night and in a foggy situation. Therefore, this research aims to detect obstacles (cars) at a railroad crossing using the Histogram of Oriented gradient (HOG) method and the Support Vector Machine (SVM) classifier. HOG functions to extract object features (cars), while SVM is responsible for classifying car objects whether they fit the criteria of car features or not. The results show that an accuracy rate of car objects was 85%, 73% for empty train tracks and 91% for detection of passing trains.