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

Year, pages: 2017, 793 - 814

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.

Zhou, X., Yuan, J., Liu, H. 2017. Real-Time Traffic Light Recognition Based on C-HOG Features. In Computing and Informatics, vol. 36, no.4, pp. 793-814. 1335-9150. DOI: https://doi.org/10.4149/cai_2017_4_793

Zhou, X., Yuan, J., Liu, H. (2017). Real-Time Traffic Light Recognition Based on C-HOG Features. Computing and Informatics, 36(4), 793-814. 1335-9150. DOI: https://doi.org/10.4149/cai_2017_4_793