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QoS-oriented Service Management in clouds for large scale industrial activity recognition

机译:QoS导向服务管理在云中进行大规模工业活动识别

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Motivated by the need of industrial enterprises for supervision services for quality, security and safety guarantee, we have developed an Activity Recognition Framework based on computer vision and machine learning tools, attaining good recognition rates. However, the deployment of multiple cameras to exploit redundancies, the large training set requirements of our time series classification models, as well as general resource limitations together with the emphasis on real-time performance, pose significant challenges and lead us to consider a decentralized approach. We thus adapt our application to a new and innovative real-time enabled framework for service-based infrastructures, which has developed QoS-oriented Service Management mechanisms in order to allow cloud environments to facilitate real-time and interactivity. Deploying the Activity Recognition Framework in a cloud infrastructure can therefore enable it for large scale industrial environments.
机译:由于工业企业的需求为质量,安全和安全保障的工业企业,我们开发了一种基于计算机视觉和机器学习工具的活动识别框架,达到了良好的识别率。但是,多个摄像机的部署到利用冗余,我们的时间序列分类模型的大型培训设定要求,以及一般资源限制以及强调实时性能,构成重大挑战并引导我们考虑分散的方法。因此,我们将我们的应用程序适应了一种新的和创新的实时支持基于服务的基础架构的框架,这开发了QoS导向的服务管理机制,以便允许云环境促进实时和交互性。因此,在云基础架构中部署活动识别框架可以为大规模的工业环境启用它。

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