Detection of Bikers without Helmet Using Image Texture and Shape Analysis

Document Type : Original Article


1 Faculty of Computer Engineering and IT, Shahrood University of Technology, Shahrood, Iran

2 Computer Science Department, Faculty of Mathematics and Computer, ‎Higher Education Complex of Bam, Bam, Iran


Helmet are essential for preventing head injuries in bikers. Traffic laws are applied in most countries to bikers who don’t wear a helmet. Manually checking bikers for the usage of a helmet is a very costly and tedious task. In this regard, several helmet detection methods were developed in literature for detecting bikers violating the law in recent years. This paper proposes an image processing method based on the Local Binary Pattern (LBP), Local Variance (LV), and Histogram of Oriented Gradient (HOG) descriptors for detection of bikers without a helmet. The innovation of the proposed method is mainly on the feature extraction step, which leads the classification towards appropriately discriminating between the two classes of helmet and non-helmet. The experimental results show our method is superior to the existing methods for helmet detection. The accuracy of the proposed helmet detection method is 98.03% using the Support Vector Machine classifier.


Waranusas. R, Bundon. N, Timtong. V, Tangnoi. C, and Pattanathaburt. P, “Machine vision techniques for motorcycle safety helmet detection”, 28th International Conference on Image and Vision Computing New Zealand (IVCNZ),(2013), 35-40. doi: 10.1109/IVCNZ.2013.6726989
Doungmala. P, and Klubsuwan. K, “  Helmet wearing detection in Thailand using Haar like feature and circle hough transform on image processing”, International Conference on Computer and Information Technology (CIT), (2011), 611-614. doi:10.1109/CIT.2016.87
Dahiya. K, Singh. D, and Mohan. C. K, “Automatic detection of bike-riders without helmet using surveillance videos in real-time”, International Joint Conference on Neural Networks (IJCNN), 3046-3051, (2016),doi: 0.1109/IJCNN.2016.7727586
Silva. R, Aires. K, Santos.T, Abdala. K, Veras. R, and Soares. A,” A. Automatic detection of motorcyclists without helmet”, Latin American Computing Conference (CLEI), 1-7, (2013),doi: 10.1109/CLEI.2013.6670613
Silva. R, Aires. K, Veras. R, Santos. T, Lima. K, and Soares. A, ”Automatic motorcycle detection on public roads”, CLEI Electronic Journal, Vol. 16, No. 3, (2013), doi: 10.1109/clei.2012.6427165
Marayatr. T, and Kumhom.P, “Motorcyclist's Helmet Wearing Detection Using Image Processing”, In Advanced Materials Research, Vol. 931, (2014), 588-592, doi:10.4028/
Silva. R. V, Aires. K. R. T, and Veras. R. D.M, “Helmet detection on motorcyclists using image descriptors and classifiers”, 27th Conference on Graphics, Patterns and Images, (2014), 141-148. doi:10.1109/sibgrapi.2014.28
Shine. L, and Jiji. C.V,” Automated detection of helmet on motorcyclists from traffic surveillance videos: a comparative analysis using hand-crafted features and CNN”, Journal of Multimedia Tools and Applications, Vol. 1380, (2020), 1-21,doi: 10.1007/s11042-020-08627-w
Alaulikar. A.S, Sanathanan. S, and Modi. C.N,” An enhanced approach for detecting helmet on motorcyclists using image processing and machine learning techniques”, In Advanced Computing and Communication Technologies, Vol. 702, (2019), 109-119, doi: 10.1007/978-981-13-0680-8_11
Rubaiyat. A.H, Toma. T.T, Kalantari-Khandani. M, Rahman. S.A, Chen. L, Ye. Y, and Pan. C.S., “Automatic detection of helmet uses for construction safety”,WIC/ACM International Conference on Web Intelligence Workshops (WIW), (2016), 135-142. doi: 10.1109/WIW.2016.045
JIANG. X, XUE. H, ZHANG. L, and ZHOU. Y,” A Study of Low-resolution Safety Helmet Image Recognition Combining Statistical Features with Artificial Neural Network”, International Journal of Simulation-Systems, Science and Technology, Vol. 17, No. 37, (2016), 1473-8031, doi: 10.5013/IJSSST.a.17.37.11
Li. K, Zhao. X, Bian. J, and Tan. M,” Automatic Safety Helmet Wearing Detection”, arXiv labs:experimental projects with community collaborators, 1802.00264, (2014).
Raj. K. D, Chairat. A, Timtong. V, Dailey. M. N, and Ekpanyapong. M, “Helmet violation processing using deep learning”, International Workshop on Advanced Image Technology (IWAIT), (2018), 1-4, doi: 10.1109/IWAIT.2018.8369734
Siebert. F.W, and Lin. H,” Detecting motorcycle helmet use with deep learning”, Journal of Accident Analysis and  Prevention, Vol. 134, (2020), doi:10.1016/j.aap.2019.105319
Shakour.M. H and Tajeripour. F,” Local entropy pattern to extract textural images features”, Vol. 3, No. 2, (2016), 73-85.
Lenc. L, and Král. P,” Automatically detected feature positions for LBP based face recognition”, In IFIP International Conference on Artificial Intelligence Applications and Innovations, (2014),246-255, doi:10.1007/978-3-662-44654-6_24
Huang.D, Shan. C, Ardabilian. M, Wang. Y, and Chen. L, “Local binary patterns and its application to facial image analysis: a survey”, Journal of Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), Vol. 41, No. 6, (2011), 765-781, doi:10.1109/TSMCC.2011.2118750.
Dalal, N, and Triggs. B,” Histograms of oriented gradients for human detection”,Computer Society Conference on Computer Vision and Pattern Recognition, (2005), 886-893.
Başa. B,” Implementation of Hog Edge Detection Algorithm Onfpga's”, Procedia-Social and Behavioral Sciences, Vol. 174, (2015), 1567-1575, doi: 10.1016/j.sbspro.2015.01.806
Boser. B. E, Guyon. I. M, and Vapnik. V. N, “A training algorithm for optimal margin classifiers”, In Proceedings of the fifth annual workshop on Computational learning theory, (1992), 144-152. doi: 10.1145/130385.130401
Ellis. R. P, and Mookim. P. G, “Cross-validation methods for risk adjustment models”, Mimeo, Boston University, (2008).