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Machine learning based approach for multimedia surveillance during fire emergencies

机译：基于机器学习的消防突发期间多媒体监测方法

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摘要

Video based surveillance of manmade disasters such as fire has become very hot topic in research and it is playing an important role in the development of smart environment. The disasters like fire cause many economic and social damages. We can prevent these damages by early detection of the fire. The current advancement in embedded processing have permitted the detection of fire using vision-based i.e. Convolutional Neural Networks (CNNs) for the surveillance. Therefore, we proposed a method using machine learning techniques for Multimedia Surveillance during fire emergencies. Our proposed model has two main deep neural networks models. Firstly, we used a hybrid model made of Adaboost and many Mulit-layer perceptron (MLP) neural networks. The purpose of hybrid Adaboost-MLP model is to predict fire efficiently. This model used different sensors data like smoke, heat, and gas for training. After predicting the fire, we proposed a CNN model to detect the fire immediately. These results show that our trained model has near 91% fire detection accuracy. We can the false positive results are quite low. These results can be improved more by further training.
机译：基于视频的Manade灾害监测，如火已经变得非常热门的研究，并且在智能环境的发展中发挥着重要作用。像火一样的灾害会导致许多经济和社会损害。我们可以通过早期检测火灾来防止这些损害。嵌入式处理中的当前进步允许使用基于视觉的I.卷积神经网络（CNNS）进行监视的火灾检测。因此，我们提出了一种使用机器学习技术在消防紧急情况下进行多媒体监控的方法。我们所提出的模型有两个主要的深神经网络模型。首先，我们使用由Adaboost和许多Mulit-Layer Perceptron（MLP）神经网络制成的混合模型。混合Adaboost-MLP模型的目的是有效地预测火灾。该型号使用不同的传感器数据，如烟雾，热量和培训。在预测火灾之后，我们提出了一种CNN模型来立即检测到火灾。这些结果表明，我们的培训模型具有近91％的火灾检测精度。我们可以的假阳性结果相当低。这些结果可以通过进一步的培训更加提高。

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  • 来源
    《Multimedia Tools and Applications》 |2020年第24期|16201-16217|共17页
  • 作者

    Faisal Saeed; Anand Paul; Won Hwa Hong; Hyuncheol Seo;

  • 作者单位

    Department of Computer Science and Engineering Kyungpook National University Daegu 702-701 Korea;

    Department of Computer Science and Engineering Kyungpook National University Daegu 702-701 Korea;

    School of Architectural Civil Environmental and Energy Engineering Kyungpook National University Daegu 702-701 Korea;

    School of Architectural Civil Environmental and Energy Engineering Kyungpook National University Daegu 702-701 Korea;

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  • 原文格式 PDF
  • 正文语种 eng
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  • 关键词

    Machine learning; Multimedia surveillance; CNN; Fire detection;

    机译：机器学习;多媒体监测;CNN;火灾探测;

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