Abstract
Indonesian citizens who use motorized vehicles are increasing every year. Every motorcyclist in Indonesia must wear a helmet when riding a motorcycle. Even though there are rules that require motorbike riders to wear helmets, there are still many motorists who disobey the rules. To overcome this, police officers have carried out various operations (such as traffic operation, warning, etc.). This is not effective because of the number of police officers available, and the probability of police officers make a mistake when detecting violations that might be caused due to fatigue. This study asks the system to detect motorcyclists who do not wear helmets through a surveillance camera. Referring to this reason, the Circular Hough Transform (CHT), Histogram of Oriented Gradient (HOG), and K-Nearest Neighbor (KNN) are used. Testing was done by using images taken from surveillance cameras divided into 200 training data and 40 testing data obtained an accuracy rate of 82.5%.
Highlights
Motorized vehicles are one type of transportation used in many parts of the world, especially motorbikes
Police officers have carried out various operations
Based on Police Headquarters data in 2013, the number of motorbikes in Indonesia ware 84,732,652 units, a large number of motorbikes caused a high number of traffic accidents involving motorcycles
Summary
Motorized vehicles are one type of transportation used in many parts of the world, especially motorbikes. Based on Police Headquarters data in 2013, the number of motorbikes in Indonesia ware 84,732,652 units, a large number of motorbikes caused a high number of traffic accidents involving motorcycles. Police officers have carried out various operations (such as traffic operation, warning, etc.) This is not effective because of the number of police officers available, and the probability of police officers make mistakes when detecting violations that might be caused due to fatigue. Proposes to detect half and full helmets using Haar Like Feature and Circular Hough Transform. Based on the above problems and previous literature studies, we propose automatic helmet detection using Circular Hough Transform (CHT) for shape detection, Histogram of Oriented Gradient (HOG) for feature extraction, and K-Nearest Neighbor (KNN) for image classifier.
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