Compression of Video Streams for Transmission from the Edge to the Cloud for Video Analytics Applications

压缩视频流以从边缘传输到视频分析应用的云端

• 批准号: 538491-2019
• 负责人: Kaddoum, Georges
• 金额: $ 1.82万
• 依托单位: École de technologie supérieure
• 依托单位国家: 加拿大
• 项目类别: Engage Grants Program
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=680382
• 关键词: Compression; Video; Streams; Transmission; Edge

The project is a research collaboration with Nuvoola Inc.; a Canadian-based company specialized in Artificial Intelligence. Nuvoola is developing a multi-factor real-time cognitive analytics engine that relies on multiple sources of information and leverages artificial intelligence, cloud computing, and business rules to map these sources into actionable intelligence. Applications for this engine are considered in areas such as healthcare, security, logistics, retail, culture, finance, government, etc. Nuvoola adopted a unique AI engine design strategy that is agnostic to the gazillions of edge and cloud resources and services available. The Nuvoola's AI engine supports intelligent surveillance applications where video streams from distributed camera networks are analyzed and provides actionable intelligence. The current implementation is limited since the important temporal content in the video streams are lost, and that limits the features that Nuvoola's tools can provide. Moreover, the accuracy of the surveillance functions such as person recognition and re-identification was proven to be boosted when spatial and temporal information are fused using deep neural networks. Nevertheless, transmitting the original video streams, including spatial and temporal information, to the cloud is expensive and becomes infeasible due to bandwidth restriction. This research activity will investigate methods and algorithms to enable transmission of video streams from the Nuvoola's edge device to the cloud, where a trade-off between the transmission bandwidth and the quality of the transmitted content is optimized. In particular, for achieving acceptable recognition rates, high quality of the Regions of Interest (ROI), e.g., faces, objects, etc., is required while the quality of the background scene is not important. The anticipated results from this research project will allow for sending video streams from the edge devices to the cloud services over weak transmission channels where video analytics and recognition algorithms are still functional since the important ROI are sent with sufficient quality.

该项目是与Nuvoola Inc.的研究合作;一家总部位于哥伦比亚的人工智能公司。Nuvoola正在开发一个多因素实时认知分析引擎，该引擎依赖于多个信息源，并利用人工智能、云计算和业务规则将这些信息源映射为可操作的智能。该引擎的应用领域包括医疗保健、安全、物流、零售、文化、金融、政府等领域，Nuvoola采用了独特的AI引擎设计策略，对大量可用的边缘和云资源和服务不敏感。Nuvoola的人工智能引擎支持智能监控应用，对来自分布式摄像机网络的视频流进行分析，并提供可操作的智能。目前的实现是有限的，因为视频流中的重要时间内容丢失了，这限制了Nuvoola的工具可以提供的功能。此外，当使用深度神经网络融合空间和时间信息时，人员识别和重新识别等监控功能的准确性得到了提高。然而，将包括空间和时间信息的原始视频流传输到云是昂贵的，并且由于带宽限制而变得不可行。这项研究活动将研究将视频流从Nuvoola边缘设备传输到云端的方法和算法，在云端优化传输带宽和传输内容质量之间的权衡。特别地，为了实现可接受的识别率，需要高质量的感兴趣区域（ROI），例如，面孔、物体等，而背景场景的质量并不重要。该研究项目的预期结果将允许通过弱传输通道将视频流从边缘设备发送到云服务，其中视频分析和识别算法仍然有效，因为重要的ROI以足够的质量发送。