Helmet Monitoring System using Hough Circle and HOG based on KNN

  • Rachmad Jibril Al Kautsar Brawijaya University
  • Fitri Utaminingrum Brawijaya University
  • Agung Setia Budi Brawijaya University

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%.

Downloads

Download data is not yet available.

References

[1] Badan Pusat Statistik Kota Salatiga, “Salatiga Dalam Angka Tahun 2013,” pp. 1, 115, 155, 2013.
[2] L. Gicquel, P. Ordonneau, E. Blot, C. Toillon, P. Ingrand, and L. Romo, "Description of various factors contributing to traffic accidents in youth and measures proposed to alleviate recurrence," Frontiers of Psychiatry, vol. 8, no. JUN, pp. 1–10, 2017, doi: 10.11591/ijeei.v6i4.463
[3] P. Doungmala and K. Klubsuwan, "Half and Full Helmet Wearing Detection in Thai- land using Haar Like Feature and Circle Hough Transform on Image Processing Pathasu," Proc. - 2016 16th IEEE Int. Conference on Computer and Information Technology CIT 2016, 2016 6th International Symposium Cloud and Service Computing IEEE SC2 2016 2016 International Symposium Security and Privacy in Social Networks and Big Data, pp. 611–614, 2017, doi: 10.1109/CIT.2016.87
[4] L. J. L. C. Wen C. Chiu S., "The safety helmet detection for ATM's surveillance system via the modified Hough transform," Proceedings of Annual IEEE International Carnahan Conference on Security Technology, pp. 259–263, 2003, doi: 10.1109/CCST.2003.1297588
[5] A. H. M. Rubaiyat et al., "Automatic detection of helmet uses for construction safety," Proceedings - 2016 IEEE/WIC/ACM International Conference on Web Intelligence Workshops, WIW 2016, no. November, pp. 135–142, 2017, doi: 10.1109/WIW.2016.10
[6] T. Kumar and K. Verma, "A Theory Based on Conversion of RGB image to Gray image," International Journal of Computer Applications., vol. 7, no. 2, pp. 5–12, 2010, doi: 10.5120/1140-1493
[7] H. Liu, Y. Qian, and S. Lin, "Detecting persons using hough circle transform in surveillance video," VISAPP 2010 - Proceedings of the International Conference on Computer Vision Theory and Applications, vol. 2, no. January, 2010, doi: 10.5220/0002856002670270
[8] L. H. Liew, B. Y. Lee, and M. Chan, "Cell detection for bee comb images using Circular hough transformation," CSSR 2010 - 2010 International Conference on Science and Social Research, no. Cssr, pp. 191–195, 2010, doi: 10.1109/CSSR.2010.5773764
[9] Pei-Yin Chen, Chien-Chuan Huang, Chih-Yuan Lien, and Yu-Hsien Tsai, "An Efficient Hardware Implementation of HOG Feature Extraction for Human Detection," IEEE Transactions on Intelligent Transportation Systems, vol. 15, no. 2, pp. 656–662, 2014, doi: 10.1109/TITS.2013.2284666
[10] K. N. Stevens, T. M. Cover, and P. E. Hart, "Nearest Neighbor Pattern Classification," vol. IT-13, no. 1, pp. 21–27, 1967.
[11] J. Maillo, S. Ramírez, I. Triguero, and F. Herrera, "kNN-IS: An Iterative Spark-based design of the k-Nearest Neighbors classifier for big data," Knowledge-Based System, vol. 117, pp. 3–15, 2017, doi: 10.1016/j.knosys.2016.06.012
[12] F. A. Mufarroha and F. Utaminingrum, "Hand Gesture Recognition using Adaptive Network Based Fuzzy Inference System and K-Nearest Neighbor," International Journal of Technology, vol. 8, no. 3, p. 559, 2017, doi: 10.14716/ijtech.v8i3.3146
[13] J. Kim, B.S. Kim, and S. Savarese, "Comparing Image Classification Methods: K-Nearest-Neighbor and Support-Vector-Machines," Applied Mathematics in Electrical and Computer Engineering, pp. 133–138, 2012.
[14] P. Wonghabut, J. Kumphong, T. Satiennam, R. Ung-Arunyawee, and W. Leelapatra, "Automatic helmet-wearing detection for law enforcement using CCTV cameras," IOP Conference Series: Earth and Environmental Science, vol. 143, no. 1, 2018. doi: 10.1088/1755-1315/143/1/012063
[15] S. Tiwari, "Blur classification using segmentation based fractal texture analysis," Indonesian Journal of Electrical Engineering and Informatics, vol. 6, no. 4, pp. 373–384, 2018. doi: 10.11591/ijeei.v6i4.463
Published
2021-03-29
How to Cite
AL KAUTSAR, Rachmad Jibril; UTAMININGRUM, Fitri; BUDI, Agung Setia. Helmet Monitoring System using Hough Circle and HOG based on KNN. Lontar Komputer : Jurnal Ilmiah Teknologi Informasi, [S.l.], v. 12, n. 1, p. 13-23, mar. 2021. ISSN 2541-5832. Available at: <https://ojs.unud.ac.id/index.php/lontar/article/view/68130>. Date accessed: 12 may 2021. doi: https://doi.org/10.24843/LKJITI.2021.v12.i01.p02.