Learning local histogram representation for efficient traffic sign recognition

Jinlu Gao, Yuqiang Fang, Xingwei Li
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引用次数: 3

Abstract

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.
学习局部直方图表示的有效交通标志识别
随着智能汽车技术的兴起,交通标志识别成为计算机视觉中的一个重要问题。针对现实场景下的交通标志识别问题,提出了一种新的局部特征表示方法,以提高交通标志识别性能。特别是以局部直方图特征为基本单元,提出了一种基于直方图交集核的字典学习方法进行特征量化。为了提高计算效率,提出了一种基于查找表的快速特征编码方法。该方法在多个离线交通标志数据库中均取得了较好的识别效果,并已扩展到现实视频中的交通标志识别。大量的实验证明了新方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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