Real-Time Traffic Light Recognition Based on C-HOG Features

Authors

  • Xuanru Zhou Beijing Key Laboratory of Information Services, Beijing Union University
  • Jiazheng Yuan Scientific Research Office, Beijing Open University
  • Hongzhe Liu Beijing Key Laboratory of Information Services, Beijing Union University

Keywords:

C-HOG features, SVM, traffic light recognition, intelligent vehicles

Abstract

This paper proposes a real-time traffic light detection and recognition algorithm that would allow for the recognition of traffic signals in intelligent vehicles. This algorithm is based on C-HOG features (Color and HOG features) and Support Vector Machine (SVM). The algorithm extracted red and green areas in the video accurately, and then screened the eligible area. Thereafter, the C-HOG features of all kinds of lights could be extracted. Finally, this work used SVM to build a classifier of corresponding category lights. This algorithm obtained accurate real-time information based on the judgment of the decision function. Furthermore, experimental results show that this algorithm demonstrated accuracy and good real-time performance.

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Published

2017-11-29

How to Cite

Zhou, X., Yuan, J., & Liu, H. (2017). Real-Time Traffic Light Recognition Based on C-HOG Features. COMPUTING AND INFORMATICS, 36(4), 793–814. Retrieved from http://www.cai.sk/ojs/index.php/cai/article/view/2017_4_793