Detection of level crossing barriers using the histogram of oriented gradients method and support vector machine

Abstract

Abstract 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.