利用深度学习作为执法盟友

D. Ramani, M. Nirmala, Sourabh V.N, Shreyas Chaudhary, Deepesh Kumar
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引用次数: 0

摘要

在逃犯犯罪或违法后寻找他们需要时间和精力。鉴于任何国家的人口密度不断上升和国土面积不断扩大,执法当局独自完成这项工作都是一项挑战。因此,公众参与变得至关重要、具有革命性和有益。这种循环是时间和工作认真。在本文中,我们试图使用深度学习和Heroku云(即云计算)提出一种不同的罪犯识别框架,假设我们的犯罪控制组织使用云计算,将帮助他们从闭路电视图像或公众上传的图像中抓住罪犯,如果看到任何地方。这个系统是为了协助抓捕罪犯和任何可以上传信息表明他们在特定地点和时间见过相关个人的人。在印度,由于光线、天气和特定方向等因素,情况总是在变化,现有的解决方案使用传统的人脸识别计算,这可能会有问题,因为没有公开的公共贡献。我们的研究论文采用了LBPH、深度学习和Heroku云技术来构建系统。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Utilising Deep Learning as a Law Enforcement Ally
Finding fugitive offenders after they have committed a crime or an illegal act takes time and effort. It is challenging for law enforcement authorities to complete this work on their own given the rising population density and the size of any nation's landmass. Thus, public participation becomes crucial, revolutionary, and beneficial. This cycle is both times and works seriously. In this paper, we tried to suggest a different framework for criminal Distinguishing & Recognition using Deep learning and Heroku Cloud, i.e. Cloud Computing, which, assuming it is used by our Crime Control Organizations, would help them catch criminals from CCTV images or images uploaded by the public if seen anywhere. This system is in place to assist in capturing criminals and anyone who can upload information indicating that they saw the relevant individual at a specific location and time. In India, where conditions are always changing due to things like light, weather, and specific directions, existing solutions use conventional face acknowledgement computations, which might be problematic because there is no open public contribution. Our research paper employs LBPH, Deep Learning, and Heroku Cloud technologies to construct the system.
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