基于分块自适应融合特征的交通标志识别

摘要

交通标志由外部轮廓和内部指示符号组成,HOG特征可较好描述图像轮廓但易受噪声影响,而LBP特征对图像细节刻画好,提出基于分块HOG-LBP自适应融合特征的交通标志识别方法.通过分块计算梯度直方图得到的权重系数,来判断该块是属于轮廓还是内部指示,对前者选择HOG权重大,后者选择LBP特征权重大,将自适应串行融合后的特征送入支持向量机识别.仿真实验结果表明,该算法对标准交通标志识别率可达到100%,对含模糊、残缺、遮挡等非标准交通标志也达到了76%.%Traffic signs consist of outer shapes and internal designated symbols, and the former can be described by Histo-gram of Oriented Gradients(HOG)which, however, is influenced by noise, and details of the latter can be well depicted by Local Binary Patterns(LBP). This paper, based on blocking HOG-LBP fusion features, proposes self-adaptation identi-fication method of traffic signs. Firstly, it through weight coefficient which is attained by calculating blocking HOG, makes a judgement of which part some certain block belongs to(outer shapes incline to adopt HOG, and internal designated symbols, the LBP features), and secondly, it puts features created by mixing self-adaptation serials into support vector machine to conduct recognition. Simulation experiment shows that this algorithm achieves 100% recognition rate of standard traffic signs, and 76%of nonstandard traffic signs including unclear, incomplete and covered ones.