融合显著性因子的行人纹理提取

针对基于纹理信息的行人特征提取算法中存在特征信息冗余度大,无法刻画人眼视觉敏感性的不足,提出一种融合人类视觉感知特性的基于显著性局部二值模式(SF-LBP)的行人纹理特征提取算法.该算法首先采用显著性计算方法提取感兴趣区域得到各部分的显著性因子;然后将显著性因子权值与行人纹理特征根据核函数相融合,生成基于SF-LBP算子的特征向量;接着统计不同区域的特征向量,形成特征直方图;最后结合自适应AdaBoost分类器构建实验平台进行实验.INRIA数据集中的实验结果显示,SF-LBP特征在检测准确率上比梯度直方图(HOG)特征、Haar特征高出2%~3%,达到97%,召回率达到90%,提高了2%左右,表明SF-LBP算子能够准确描述行人的纹理特征,提高行人检测系统的准确率.

Pedestrian texture extraction by fusing significant factor

The algorithm of extracting pedestrian features based on texture information has the problems of redundant feature information and being unable to depict the human visual sensitivity, an algorithm named SF-LBP was proposed to extract pedestrian texture feature by Significant Local Binary Pattern which fuses the characteristics of human visual pedestrian system. Firstly, the algorithm calculated the significant factor in each region by saliency detection method. Then, it rebuilt the eigenvector of the image by significant factor weight and pedestrian texture feature, and generated the feature histogram according to local feature. Finally it integrated adaptive AdaBoost classifier to construct pedestrian detection system. The experimental results on INRIA database show that the SF-LBP feature achieves a detection rate of 97% and about 2%-3% higher than HOG (Histogram of Oriented Gradients) feature and Haar feature. It reaches recall rate of 90% and 2% higher than other features. It indicates that the SF-LBP feature can effectively describe the texture characteristics of pedestrians, and improve the accuracy of the pedestrian detection system.