使用CNN模型识别可疑和犯罪行为-对当前研究趋势的调查

Zhunis Karimov, Nauryzbay Sapargali, Nazerke Manteyeva, Mels Begenov, Birzhan Moldagaliyev
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引用次数: 0

摘要

近年来,在分析录像以识别犯罪和可疑行为方面取得了重大进展。在这些研究中使用的机器学习架构的主要类型是2D和3D卷积神经网络(CNN)架构。这篇文章的目的是回顾一般的CNN的概念,以及他们的各种修改用于从视频片段的犯罪侦查。除了对架构细节的介绍外,本文还重点研究和比较了利用CNN神经架构检测犯罪行为的各种研究中使用的方法。最后,提出了该领域未来可能的研究方向。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Using CNN Models to Identify Suspicious and Criminal Behavior - A Survey of Current Research Trends
In recent years there was significant progress in analyzing video recordings to identify criminal and suspicious behavior. Major types of machine learning architectures used in these studies are 2D and 3D Convolutional Neural Network (CNN) architecture. This article aims to review a notion of general CNN’s as well as their various modifications used for crime detection from video footage. In addition to the introduction to architectural details, this paper is focused on studying and comparing methods used in various studies on detecting criminal behavior using CNN neural architecture. Finally, the paper presents possible directions for future research in the given domain.
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