Learning Local Histogram Representation for Efficient Traffic Sign Recognition

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.