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作 者:张文胜 杨志超 ZHANG Wensheng;YANG Zhichao(The First Affiliated Hospital of Guangzhou University of Chinese Medicine,Guangzhou 510405,Guangdong Province,China)
机构地区:[1]广州中医药大学第一附属医院,广东广州510405
出 处:《长江信息通信》2025年第2期118-120,共3页Changjiang Information & Communications
摘 要:由于现有的系统实时监测效果不理想,正确识别数量低。为此,本研究提出基于红外技术的医院住院部智能安防系统设计方法。在系统硬件方面,系统芯片选用的是GR451D,同时配备了FLASH和SRAM存储数据。运用DA转换器进行数模信号转换,并将采集到的图像数据使用显示屏通过PAL模拟视频接口同步输出,共同完成红外图像的具体展示;在系统软件方面,对目标图像进行预处理,提升图像的细节,通过计算图像中目标与背景的强度差异来进行目标识别。采用k-mean聚类方法进行聚类,输出最终的聚类结果,判断行为属于常见行为还是异常行为。测试结果表明,在15:35这一时间点观察到系统监测到的数据流小于4000的异常情况,实时监测到存在行为异常问题;医院工作人员人脸正确识别数量能够达到实际要求,更加精确地识别和管理工作人员,提高安全管理的效率。Due to the poor real-time monitoring performance of existing systems,the number of correct identifications is low.Therefore,this study proposes a design method for intelligent security systems in hospital inpatient departments based on infrared technology.In terms of system hardware,the system chip is GR451D,equipped with FLASH and SRAM for data storage.Using a DA converter for digital to analog signal conversion,and synchronously outputting the collected image data through the PAL analog video interface on the display screen,jointly completing the specific display of infrared images;In terms of system software,target images are preprocessed to enhance image details,and target recognition is performed by calculating the intensity difference between the target and background in the image.Using the k-means clustering method for clustering,output the final clustering results,and determine whether the behavior belongs to common or abnormal behavior.The test results indicate that at 15:35,an abnormal situation with a data flow of less than 4000 was observed in the system,and abnormal behavior issues were detected in real-time;The number of correct facial recognition for hospital staff can meet the actual requirements,more accurately identify and manage staff,and improve the efficiency of safety management.
分 类 号:TP274[自动化与计算机技术—检测技术与自动化装置]
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