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CNN based Smart Surveillance System: A Smart IoT Application Post Covid-19 Era

机译:基于CNN的智能监控系统:Covid-19时代之后的智能物联网应用

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There are many applications available in the face detection algorithm but very limited applications are identified for further processing. When it comes to identifying faces in the crowd and that too in all-weather conditions then it's too difficult a task to be conducted. Considering this challenge, most of the surveillance systems are not automated. In the sense the CCTV deployed are used only for bufferingpurposes. Very rarely an event is brought to the notice and later CCTV footage is used as a tool for legal issues. There are a lot of concerns about deploying CCTV's in Public places as well. Selecting this system, our work have made sure that the CCTV's will be used to process the video being taken, and whenever an event triggers, notice(s) has to be provided for officials. Thus, the proposed system is for automatic detection and recognition of human faces for finding criminals/suspects/missing persons for surveillance. The intended method first detects a face in the video using a face detection algorithm and then it will search whether the face is present in the data centre. The method provides the ability to detect, extract features, and recognize a face from inputs taken by camera or video automatically. Recognizing faces under different natural conditions can be done by training the system on a limited number of facial images. Also, this system is tested for Covid-19 Post situations, wherein wearing masks in public places is compulsory thus face recognition in these aspects is also tested, and encouraging results have been achieved.
机译:人脸检测算法中有许多应用程序可用,但是识别出的应用程序非常有限,需要进一步处理。当要识别人群中的人脸以及在全天候条件下识别人脸时,执行任务就太困难了。考虑到这一挑战,大多数监视系统不是自动化的。从某种意义上说,部署的CCTV仅用于缓冲目的。很少有事件引起注意,后来的闭路电视录像被用作解决法律问题的工具。对于在公共场所部署CCTV也有很多担忧。选择此系统后,我们的工作已确保将使用CCTV来处理正在拍摄的视频,并且每当事件触发时,都必须向官员提供通知。因此,所提出的系统用于自动检测和识别人脸,以寻找犯罪分子/嫌疑犯/失踪者进行监视。预期的方法首先使用面部检测算法检测视频中的面部,然后将搜索数​​据中心中是否存在该面部。该方法提供了从相机或视频自动获取的输入中检测,提取特征和识别面部的能力。通过在有限数量的面部图像上训练系统,可以在不同的自然条件下识别面部。此外,该系统还针对Covid-19 Post情况进行了测试,其中必须在公共场所佩戴口罩,因此还测试了这些方面的面部识别,并获得了令人鼓舞的结果。

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