One Shoot In-Door Surveillance Module Based On MCA Associative Memory

Ghassan Ahmed Mubarak, Emad I Abdul Kareem
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Abstract

Several studies and researches had been made to develop In-Door Surveillance module. Most of them had depended on traditional techniques tend to be more complicated. After revolutionary development in Artificial intelligence that simulates the human brain, many industrial companies has started to build more efficient and intelligent surveillance systems depending on artificial intelligence techniques. Thus, this research would deal with an Indoor surveillance module depending on non-traditional techniques, which is Multi-Connect Architecture Associative Memory (MMCA). The proposed module would process any given pre-processed image-stream and decide whether it is secured or non-secured case. This process had been done by training the proposed module with one selected secured image. The study found that accuracy values were between (74.6 – 97.2%). Accuracy was almost around 95% which is considered a promising results in real-time of execution.
一种基于MCA联想记忆的室内拍摄监控模块
对室内监控模块的开发进行了若干研究。他们大多依靠传统的技术,往往更复杂。在模拟人类大脑的人工智能取得革命性发展之后,许多工业公司已经开始依靠人工智能技术建立更高效、更智能的监控系统。因此,本研究将处理基于非传统技术的室内监控模块,即多连接架构联想记忆(MMCA)。提出的模块将处理任何给定的预处理图像流,并决定它是安全的还是非安全的情况。这个过程是通过用一个选定的安全图像训练所提议的模块来完成的。研究发现,准确率值在(74.6 - 97.2%)之间。准确率几乎在95%左右,这被认为是实时执行的一个有希望的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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