摘要
在建筑、通信、电力等工程行业中,作业人员需要频繁执行室外作业。由于室外环境复杂,许多运维工作存在来自高压、高空、深坑等因素的高风险。安全事故一旦发生,将造成巨大的人员和财产损失。因此,需要在作业过程中对作业人员进行身份核验,以方便监督。然而,在传统的监督方式中,作业现场的人员管理和行为管控完全依靠人工核查,监控视频也依赖人工看守,无法做到人员身份实时核验以及对非作业人员入场的有效预警。针对室外作业场景中的作业人员身份识别,目前的研究方法大多基于人脸识别。人脸识别方法能够在作业人员脸部信息清晰且完整时准确识别出其身份信息。然而,当存在遮挡,以及受检测距离、检测角度等因素影响时,会造成脸部信息不完全或者模糊,导致采用人脸识别方法难以准确识别出作业人员的身份。步态特征是一种描述行走方式的复杂行为特征,包括脚的触地时间、离地时间和人体高度、双手摆动幅值等。相比人脸识别,步态特征识别有以下优点:第一,步态识别适用的检测距离更远,而人脸特征随着检测距离的增加识别难度明显上升;第二,步态特征识别是非主动识别,现场作业人员几乎随时随地处于行走状态,而人脸识别需要识别对象正对检测装置;第三,步态特征具有较强的特异性,不像人脸特征较易被模仿、修改。不过,单独采用步态特征进行识别,虽然在作业人员运动时可以捕捉体态信息进而较为准确地对其进行身份核验,但是无法对静态的作业人员进行身份核验。针对此问题,提出了一种多特征融合的身份核验方法,结合步态与人脸等多特征进行识别,不受衣着、环境等外在因素的干扰,可以有效提高身份核验的准确率。提出的融合人脸特征和步态特征的多特征身份核验方法包括身份注册阶段、训练阶段与测试验证�
In construction, communications, power and other engineering industries, workers need to perform outdoor operations frequently.Due to the complex outdoor environment, there are high risks from factors like high voltage, high altitude, and deep pits in many operation and maintenance tasks.Once an accident happens, huge losses in personnel and property would be caused.Therefore, it is necessary to verify identities of operators during the operation process for supervision.However, in the traditional supervision method, the personnel management and behavior control in the operation scenes rely entirely on manual verification, and the surveillance video also relies on manual guards.It is impossible to achieve real-time verification of personnel identities and effective warning of the entry of non operators.For the identification of workers in outdoor work scenes, most of the current research methods are based on the face recognition.Face recognition method can accurately identify the identity information of a worker when his facial information is clear and complete.However, when the facial information is incomplete or fuzzy because of occlusion, long detection distance or the inclined detection angle, it will be difficult to accurately identify the operator’s identity with the face recognition method.Gait feature is a complex behavioral feature of a walking person, including the time the foot touch and leave the ground, the human height, and the swing amplitude of hands.Compared with face recognition, gait recognition has the following advantages.Firstly, the distance applicable to gait recognition is longer, while the recognition difficulty of facial features increases as the detection distance increases.Secondly, gait feature recognition is non-active, and workers on the operation scene are walking almost anytime and anywhere, but face recognition expects the recognition object to face the detection device.Thirdly, gait features have strong specificity and are difficult to be imitated and modified.Nevertheless, g
作者
王刘旺
郑礼洋
叶晓桐
郭雪强
WANG Liuwang;ZHENG Liyang;YE Xiaotong;GUO Xueqiang(State Grid Zhejiang Electric Power Company Limited Research Institute,Hangzhou,Zhejiang 310014,China;College of Control Science and Engineering,Zhejiang University,Hangzhou,Zhejiang 310027,China)
出处
《河北科技大学学报》
CAS
北大核心
2021年第6期635-642,共8页
Journal of Hebei University of Science and Technology
基金
国网浙江省电力有限公司科技资助项目(5211DS19002K)
国家自然科学基金基础科学中心资助项目(62088101)。
关键词
模式识别
身份核验
多特征融合
室外作业场景
人脸识别
步态特征
pattern recognition
identity verification
multi feature fusion
outdoor operation scenes
face recognition
gait features
