基于多级特征的红外图像行人检测算法  

Pedestrian Detection Algorithm for Infrared Image Based on Multi-level Features

作　　者：张驰[1] 谭南林[1] 李国正[1] 苏树强[1] ZHANG Chi;TAN Nanlin;LI Guozheng;SU Shuqiang(School of Mechanical,Electronic and Control Engineering,Beijing Jiaotong University,Beijing 100044,China)

机构地区：[1]北京交通大学机械与电子控制工程学院,北京100044

出　　处：《计算机工程》2020年第4期260-265,共6页Computer Engineering

基　　金：国家自然科学基金(61527812)。

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.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.

关 键 词：红外图像 行人检测 显著性检测 ZERNIKE矩 卷积神经网络 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]