关键词: 交通标志识别, 自适应融合特征, 方向梯度直方图（HOG）, 局部二值模式（LBP）

Abstract: Traffic signs consist of outer shapes and internal designated symbols, and the former can be described by Histogram 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 identification 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.

Key words: recognition of traffic signs, self-adaptation fusion features, Histogram of Oriented Gradients（HOG）, Local Binary Patterns（LBP）