• Zamani Md Sani Universiti Teknikal Malaysia Melaka
  • Zamani Md Sani Universiti Teknikal Malaysia Melaka
  • Mohd Shahrul Hakimi Isa Universiti Teknikal Malaysia Melaka


HOG Descriptor, Traffic light Arrow, SVM Classifier, Machine Learning, Image Processing


Abstract Autonomous intelligent vehicles are keep expanding under a real development in this era after many researches have been made to implement a better safety features in a transportation sector. In this paper, an algorithm of recognition of the traffic light arrow in the daytime specifically for the operation of the video system has been proposed using the technology of the image processing and machine learning. In the machine learning technique, the Support Vector Machine, SVM classifier has been used for the learning process and conduct a classification process as well. Firstly, the images are converted to Hue Saturation Value (HSV) color space while the color thresholding has been used to obtain the region of interest for the traffic light arrow in the image. In the learning process, each of the traffic light arrows in the dataset will go through the pre-processing phase, detection phase before going through the hog descriptor for feature extraction purpose. Prior to send the image to the SVM, the hog descriptor process is to extract the features for each of the shape of the traffic light arrow. As a result, this algorithm has achieved 98.52% accuracy, 1.48% error, 98.52% precision and 98.68% of recall through the testing process.

Author Biography

Zamani Md Sani, Universiti Teknikal Malaysia Melaka

Fakulti Kejuruteraan Elektrik, Mechatronic Department

Senior Lecturer


T. Bücher et al., “Image processing and behavior

planning for intelligent vehicles,” IEEE Trans. Ind. Electron., vol. 50, no. 1, pp. 62–75, Feb.

, doi: 10.1109/TIE.2002.807650.

C. Yu and Y. Bai, “A traffic light detection method,” Adv. Intell. Soft Comput., vol. 163 AISC, pp. 745–751, 2012, doi: 10.1007/978-3- 642-29458-7_104.

Z. Chen and X. Huang, “Accurate and Reliable Detection of Traffic Lights Using Multiclass Learning and Multiobject Tracking,” IEEE Intell. Transp. Syst. Mag., vol. 8, no. 4, pp. 28–42, Dec. 2016, doi: 10.1109/MITS.2016.2605381.

K. H. Lu, C. M. Wang, and S. Y. Chen, “Traffic light recognition,” J. Chinese Inst. Eng. Trans. Chinese Inst. Eng. A/Chung-kuo K. Ch’eng Hsuch K’an, vol. 31, no. 6, pp. 1069–1075, 2008, doi: 10.1080/02533839.2008.9671460.

S. Salti, A. Petrelli, F. Tombari, N. Fioraio, and

L. Di Stefano, “Traffic sign detection via interest region extraction,” Pattern Recognit., vol. 48, no. 4, pp. 1039–1049, Apr. 2015, doi: 10.1016/j.patcog.2014.05.017.

B. Advisor and L. Reznik, “Traffic Light Detection,” 2016.

Y. Y. Qin, W. Cui, Q. Li, W. Zhu, and X. G. Li, “Traffic Sign Image Enhancement in Low Light Environment,” in Procedia Computer Science, 2018, vol. 154, pp. 596–602, doi: 10.1016/j.procs.2019.06.094.

X. Shi, N. Zhao, and Y. Xia, “Detection and classification of traffic lights for automated setup of road surveillance systems,” Multimed. Tools Appl., vol. 75, no. 20, pp. 12547–12562, Oct. 2016, doi: 10.1007/s11042-014-2343-1.

M. Diaz-Cabrera, P. Cerri, and P. Medici, “Robust real-time traffic light detection and distance estimation using a single camera,” Expert Syst. Appl., vol. 42, no. 8, pp. 3911–3923, May 2015, doi: 10.1016/j.eswa.2014.12.037.

G. Mu, Z. Xinyu, L. Deyi, Z. Tianlei, and A. Lifeng, “Traffic light detection and recognition for autonomous vehicles,” J. China Univ. Posts Telecommun., vol. 22, no. 1, pp. 50–56, 2015, doi: 10.1016/S1005-8885(15)60624-0.

M. Haag and H.-H. Nagel, “Incremental recognition of traffic situations from video image sequences.” [Online]. Available:

P. H. Kassani and A. B. J. Teoh, “A new sparse model for traffic sign classification using soft histogram of oriented gradients,” Appl. Soft Comput. J., vol. 52, pp. 231–246, Mar. 2017, doi: 10.1016/j.asoc.2016.12.037.

Institute of Electrical and Electronics Engineers, 2018 International Conference on Information and Communications Technology (ICOIACT). .

S. Kaplan Berkaya, H. Gunduz, O. Ozsen, C. Akinlar, and S. Gunal, “On circular traffic sign detection and recognition,” Expert Syst. Appl., vol. 48, pp. 67–75, Apr. 2016, doi: 10.1016/j.eswa.2015.11.018.

X. Zhou, J. Yuan, H. Liu, X. Zhou, J. Yuan, and

H. Liu, “REAL-TIME TRAFFIC LIGHT RECOGNITION BASED ON C-HOG FEATURES,” Comput. Informatics, vol. 36, pp. 793–814, 2017, doi: 10.4149/cai.




How to Cite

Md Sani, Z., Md Sani, Z., & Isa, M. S. H. (2020). TRAFFIC LIGHT ARROW SHAPE RECOGNITION USING HOG DESCRIPTORS AND SVM CLASSIFIER. International Journal of Electrical Engineering and Applied Sciences (IJEEAS), 3(2), 53–60. Retrieved from



Mechatronics and Intelligent Robotics