改进的多光谱聚合通道行人检测

针对外部环境的多变性和复杂性导致的单一波段下行人检测准确率较低的问题，提出了一种改进的基于可见和红外双波段聚合通道特征的行人检测算法。分别提取可见图像与红外图像的聚合通道特征；通过改变像素对比规则，采用自适应的阈值进行比较，将得到的改进的中心对称的局部二值模式特征添加到特征通道中；针对多光谱聚合通道特征设计了不同滤波器组进行滤波；训练分类器，实现多光谱下行人检测。实验表明，改进的局部二值模式特征能更好地描述红外图像中行人的对称性，中间滤波层丰富了候选特征池，算法在多种场景均能有效检测出行人，提高了行人检测精度，与利用多光谱聚合积分通道的检测工作相比，平均漏检率有所降低。

Improved multispectral aggregate channel for pedestrian detection

To solve the problem of low pedestrian detection accuracy in a single band due to variability and complexity of external environment, an improved pedestrian detection algorithm based on multispectral aggregate channel feature is proposed. The aggregate channel features of visible images and infrared images are extracted, respectively. The pixel contrast rule is changed and the results are compared with the adaptive threshold. The improved central symmetric local binary pattern feature is added to the feature channels. Different filter banks are designed to filter the multispectral aggregate channel features. The classifier is trained to realize the multispectral pedestrian detection. Experiments show that the improved local binary pattern feature can describe the symmetry of pedestrians of infrared images better and the intermediate filter layer enriches the candidate feature pool. The algorithm can effectively detect pedestrians in various scenes and improve the pedestrian detection accuracy. Compared with the previous multispectral aggregate channel detection work, the algorithm reduces the log-average miss rate.