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Abstract

High number in usage of Motorcycles has led to increase in road related accidents and injuries. The major reasons for accidents is the motorcyclist not wearing a helmet. One of the method is by traffic police manually monitoring motorcyclists at road junctions for not wearing helmet or through the CCTV footage video, which requires human efforts to detect motorcyclists without helmet. This paper presents an automated machine system for detecting motorcyclists not wearing helmet from video. The system extracts moving objects by background subtraction and classifies them as a motorcycle or other moving objects based on features extracted. Then for classified motorcyclist, head portion is located and it is classified as helmet or non-helmet. The system uses Convolutional Neural Networks trained using transfer learning on top of pre-trained model and combined with computer vision for classification which has helped in achieving greater accuracy. Our results on traffic videos show an accuracy of 98.56% on detection of motorcyclists without helmet. 

Keywords

Helmet detection; Motorcycle, Convolutional Neural Network, Computer Vision, Transfer learning.

Article Details

How to Cite
[1]
Fahad Khan, Nitin Nagori, and Ameya Naik, “HELMET PRESENCE DETECTION ON MOTORCYCLISTS USING IMAGE PROCESSING AND MACHINE VISION TECHNIQUES”, IEJRD - International Multidisciplinary Journal, vol. 5, no. 5, p. 7, Jun. 2020.

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