摘要： 由于可见光图像和红外图像的成像原理不同，可见光图像的行人检测算法难以直接应用于红外图像中。为此，提出一种基于多级梯度特征的红外图像行人检测算法。使用改进的图像显著性检测算法提取红外图像的关键区域，应用质心重定位的滑窗算法快速定位其中的高亮区，采用Zernike矩判断图像的对称性及与行人特征的相似性，通过基于边缘信息输入的卷积神经网络模型逐级缩小判定范围。在OTCBVS红外图像行人数据集上的实验结果表明，与稀疏表示算法相比，该算法的检测准确率较高。

关键词: 红外图像, 行人检测, 显著性检测, Zernike矩, 卷积神经网络

Abstract: Due to the differences of imaging principle between visible images and infrared images,pedestrian detection algorithms for visible images cannot be directly applied to infrared images.To address the problem,this paper proposes a pedestrian detection algorithm for infrared images based on multi-level features.First,the key regions of infrared images are extracted by using an improved image saliency detection algorithm,and their highlighted regions are rapidly located by using the sliding window algorithm for centroid relocation.Then the symmetry of images and their similarity to pedestrian features are judged by using Zernike moment.Finally,the regions to be judged are gradually narrowed down by using the Convolutional Neural Network(CNN) based on edge information input.Experimental results on the OTCBVS dataset of infrared pedestrian images show that compared with the sparse representation algorithm,the proposed algorithm has a higher detection accuracy rate.

Key words: infrared image, pedestrian detection, saliency detection, Zernike moment, Convolutional Neural Network(CNN)

中图分类号: 

• TP391