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

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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.