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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Cite this article

@article{cai-2123,
  author  = {Xuanru Zhou and Jiazheng Yuan and Hongzhe Liu},
  title   = {Real-Time Traffic Light Recognition Based on C-HOG Features},
  journal = {Computing and Informatics},
  volume  = {36},
  number  = {4},
  pages   = {793--814},
  year    = {2017},
  issn    = {1335-9150},
  doi     = {10.4149/cai_2017_4_793}
}
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