基于人工智能的紧急识别计算机系统

Diana Velychko, H. Osukhivska, Yuri Palaniza, Nadiia Lutsyk, Łukasz Sobaszek
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摘要

目前,人工智能在生活的许多领域都得到了应用。除了协助情报工作、解决复杂的计算问题或分析各类数据外,上述技术还可应用于为人们提供安全保障的过程中。本文提出了一种基于人工智能的紧急识别系统,旨在及时发现和通知危险情况。所提出的解决方案将人 "举起双手 "的姿势视为一种紧急情况,表明人面临潜在危险。因为人们在面临潜在危险时大多会被迫举起双手,这种姿势会吸引人们的注意,强调对某些事件的情绪反应,通常被用作危险的标志或征服的手段。系统应能识别人的姿势,检测到它,进而告知威胁。本文提出了一种基于人工智能的紧急识别系统,利用 PoseNet 机器学习模型检测人的 "举手 "姿势,以进行紧急识别。假设只利用 6 个关键点可以减少系统的计算资源,因为结论是在考虑到较少数据量的情况下得出的。在这项研究中,我们创建了一个包含 1510 幅图像的数据集,用于训练人工智能模型,并对决策进行了验证。使用监督机器学习方法对紧急情况的定义进行分类。可供选择的方法有根据准确率对支持向量机、逻辑回归、奈夫贝叶斯分类器、判别分析分类器和 K-近邻分类器进行了评估。总之,本文提出了一种全面、创新的紧急事件识别方法,以便利用所提议的系统对紧急事件做出快速反应。
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Artificial Intelligence Based Emergency Identification Computer System
The use of Artificial Intelligence is currently being observed in many areas of life. In addition to assisting in intel - lectual work, solving complex computational problems, or analyzing various types of data, the aforementioned techniques can also be applied in the process of providing security to people. The paper proposes an emergency identification system based on Artificial Intelligence that aims to provide timely detection and notification of dan - gerous situations. The proposed solution consider the position of a person “hands up” as an emergency situation that will indicate a potential danger for a person. Because people in the face of potential danger are mostly forced to raise their hands up and this pose attracts attention, emphasizes the emotional reaction to certain events and is usually used as a sign of risk or as a means of subjugation. The system should recognize the pose of a person, detect it, and consequently inform about the threat. In this paper, an AI based emergency identification system was proposed to detect the human pose “hands up” for emergency identification using the PoseNet Machine Learn - ing Model. The assumption consists that the utilization only of 6 key points made allows reducing the computing resources of the system since the conclusion is made taking into account a smaller amount of data. For the study, a dataset of 1510 images was created for training an Artificial Intelligence model, and the decisions were verified. Supervised Machine Learning methods are used to classify the definition of an emergency. Alternative methods: Support Vector Machine, Logistic Regression, Naïve Bayes Classifier, Discriminant Analysis Classifier, and K-nearest Neighbours Classifier based on the accuracy were evaluated. Overall, the paper presents a comprehensive and innovative approach to emergency identification for quick response to them using the proposed system.
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