Learning local histogram representation for efficient traffic sign recognition
Jinlu Gao, Yuqiang Fang, Xingwei Li · 2015
With the rising of intelligent vehicle technologies, traffic sign recognition become an essential problem in computer vision. Focusing on the traffic sign recognition under real-world scenario, this paper aims to develop novel local feature representation to improve the traffic sign recognition performance. Especially, with the local histogram feature as a basic unit, a novel histogram intersection kernel based dictionary learning method is proposed for feature quantization. Then a fast feature encoding approach based on look-up table is induced to improve the calculation effectiveness. The proposed recognition method achieves high performance on several off-line traffic sign databases, and has also been extended to recognize traffic sign in real-world videos. Extensive experiments have demonstrated the effectiveness of new method.