ABSTRACT
The dominant transportation vehicle in Indonesia is manual transportation. This type of transportation is controlled by the driver himself. Cases of traffic accidents are increasing due to lack of awareness of driving safety and security. The biggest factor in accidents is human error. One accident caused by human error such as a driver who lost speed control, because he ignored the maximum and minimum speed limiting signs. Therefore, the solution for this problem is creating a warning systems that can be used for recognizing maximum and minimum speed limiting signs. The system uses a raspberry pi camera to capture images then be detected and recognized the speed sign. If the system manages to recognize the signs according to the actual conditions traversed by the driver, it will get notification of speed sign figures in the form of sound from the speakers. The study applied the Histogram of Oriented Gradients (HOG) method to obtain the characteristic feature extraction from the sign, then classify it using the K-Nearest Neighbor (K-NN) method. Classification testing using K-NN consist of 650 training data and 48 test data that are comes from six sign types, there are Maks 20 km/h, Max 25 km/h, Max 30 km/h, Max 40 km/h, Max 50 km/h, Min 20 km/h). The average accuracy values is 97.91% for k=1 and 2. Meanwhile, accuracy of k = 3, 4 and 5 have similar value, that is 95.83%. The average time of computing the system to recognize objects 897 milliseconds. The average result of recognition based on the best k value is 97.91%.
- Li, M., Cao, Song, X., Huang, Y., Wang J., & Huang, Zhi., 2018. Shared Control Driver Assistance System Based on Driving Intention and Situation Assessment. Industrial Informatics.Google Scholar
- Oh, B and lee, H., 2017. Estimation Method for Advanced Driver Assistance System and Real-Time Context-Aware. Sungkyunkwan University.Google Scholar
- Marianingsih, S., Utaminingrum, F., and Bachtiar, F. A., 2019. Road Surface Types Classification Using Combination of K-Nearest Neighbor and Naive Bayes Based on GLCM. Brawijaya University.Google Scholar
- Tabernik, D., and Skocaj, D., 2019. Deep Learning for Large Scale Traffic Sign Detection and Recognition. Ljubljana University.Google Scholar
- Reinaldo, Manurung, N, Simbolon, J.I., and Christnatalis., 2018. Traffic Sign Detection Using Histogram Of Oriented Gradients and Max Margin Object Detection. Prima Indonesia University.Google Scholar
- Dai, T., Hao, Z., Wang, Z., Zhan, W., and Tang, Y., 2020. Traffic Detection and Tracking Based on Improved HOG Color Feature Fusion.. Changchun China University.Google Scholar
- Astawa, I., Caturbawa, I. G. N. B., Sajayasa, I. M., & Atmaja, I. M. A. D. S, Z., Wang, Z., Zhan, W., and Tang, Y., 2017. Detection Of License Plate Using Sliding Window, Histogram Of Oriented Gradinets, And Support Vector Machines Method. Negeri Bali Polytechnic.Google Scholar
- Han, B., Ding, H.P., Zhang, Y.X., and Zhao, Y.H., 2016. Photometric Redshift Estimation for Quasars by Integration of KNN and SVM. Wuhan University.Google Scholar
- Mufarroha, F.A and Utaminingrum, F., 2017. Hand Gesture Recognition Using Adapive Network Based Fuzzy Inference System and K-Nearest Neighbor. Brawijaya University.Google Scholar
- Khisanudin and Murianto., 2020. Dragon Fruit Maturity Detection Based HSV Space Color Using Naive Bayes Classifier Method. Ahmad Dahlan Yogyakarta University..Google Scholar
- Utaminingrum, F., Fauzi, M.A., Sari, Y.A., Adinugroho, S., Wihandika, R.C., Syauqy, D., & Adikara, P.P., 2017. Human Guide Tracking Using Combined Histogram of Oriented Gradient and Entropy Difference Minimization Algorithm for Camera Follower. Brawijaya University.Google Scholar
- Mallick, S., 2018. Learn OpenCV. [Online] Available in : <https://www.learnopencv.com/histogram-of-oriented-gradients/> [Access 10 Agustus 2019].Google Scholar
- Utaminingrum, F., Somawirata, K., & Naviri, G.D., 2018. Alphabet Sign Language Recognition Using K-Nearest Neighbor Optimization. Brawijaya University and National Institute of Technology (ITN Malang).Google Scholar
Index Terms
- Speed limiting sign recognition system using histogram of oriented gradients method and K-nearest neighbor classification based on raspberry pi
Recommendations
Nearest Neighbor-Based Classification of Uncertain Data
This work deals with the problem of classifying uncertain data. With this aim we introduce the Uncertain Nearest Neighbor (UNN) rule, which represents the generalization of the deterministic nearest neighbor rule to the case in which uncertain objects ...
Eye-Gaze Tracking Method Driven by Raspberry PI Applicable in Automotive Traffic Safety
AIMS '14: Proceedings of the 2014 2nd International Conference on Artificial Intelligence, Modelling and SimulationThis paper comes as a response to the fact that, lately, more and more accidents are caused by people who fall asleep at the wheel. Eye tracking is one of the most important aspects in driver assistance systems since human eyes hold much in-formation ...
Real-Time Highway Traffic Accident Prediction Based on the k-Nearest Neighbor Method
ICMTMA '09: Proceedings of the 2009 International Conference on Measuring Technology and Mechatronics Automation - Volume 03The occurrence of a highway traffic accident is associated with the short-term turbulence of traffic flow. In this paper, we investigate how to identify the traffic accident potential by using the k-nearest neighbor method with real-time traffic data. ...
Comments