基于改进神经网络的智能校园监控系统研究

Research on Intelligent Campus Monitoring by Using Improved Neural Network System

研究了改进神经网络在智能校园监控中的应用。针对夜间校园监控过程,使用区域亮度分析方法实现图像质量的有效增强。为提高校园监控的适应性,完成了混合高斯背景模型的构建。高校校园图像信息通过监控视频完成采集和预处理过程后,采用改进神经网络完成所需图像阈值的计算,然后对监控图像的边缘轮廓特征量进行提取,将据此获取的阈值作为信息输入实现监控图像处理过程,得出异常特征量以保证校园环境的安全,图像处理及监控过程具有延时短、实时性强、智能水平高的优势。为智能高校校园的安全管理提供参考。

This paper mainly studies the application of improved neural network in intelligent campus monitoring.The nighttime campus monitoring image achieves effective image quality enhancement through the use of regional brightness analysis method.In order to improve the adaptability of campus monitoring,a hybridg aussian model is completed.After the campus image information is collected and preprocessed by the monitoring video,the improved neural network is used to complete the calculation of the required image threshold,and then the edge contour feature quantity of the monitoring image is extracted,and the threshold obtained.The information input realizes the monitoring image processing process,and the abnormal feature quantity is obtained to ensure the safety of the campus environment.The image processing and monitoring process have the advantages of short delay,strong real-time and high intelligence level.It may provide a reference for the security management of smart campuses.