基于深度卷积神经网络的红外图像行人检测

Human detection in infrared image based on deep convolution neural network

红外图像中的行人检测一直是计算机视觉领域的研究热点与难点。针对传统的红外行人检测方法需要人工设计目标表达特征的弊端,本文从深度学习的角度出发,提出一种可以自动构建目标表达特征的红外行人检测卷积神经网络。在对卷积神经网络的实现原理进行分析的基础上,设计了红外行人检测卷积神经网络的初始结构,然后通过实验对初始结构进行调整,得到最终的检测神经网络。对实拍红外人体数据库进行行人检测的实验结果表明,该方法在保持低虚警率的同时可以对红外图像中的行人进行稳健检测,优于传统方法。

Pedestrian detection in infrared images has been a hot and difficult research topic in computer version.Traditional methods of pedestrian detection mainly depend on the manual feature for the expression of human body and the results largely relies on the feature representation.Designing artificial features is time-consuming and labor intensive,requires heuristic expertise and experience.Deep learning model based on convolution neural network can automatically learn feature representation from the original images,while avoiding the drawbacks of artificial features.Its difficulty is the choice of network parameters.In this paper,we propose to use deep learning method based on convolution neural network in the process of pedestrian detection.In addition,we analyze the impact of network layers,convolution kernel sizes and feature maps to pedestrian detection in infrared images are.The results demonstrate the superiority of our method over traditional methods in detection rate and alarm rate.