Bridging the Edge-Cloud Barrier for Real-time Advanced Vision Analytics.

Advanced vision analytics plays a key role in a plethora of real-world applications. Unfortunately, many of these applications fail to leverage the abundant compute resource in cloud services, because they require high computing resources and high-quality video input, but the (wireless) network connections between visual sensors (cameras) and the cloud/edge servers do not always provide sufficient and stable bandwidth to stream high-fidelity video data in real time. This paper presents CloudSeg, an edge-to-cloud framework for advanced vision analytics that co-designs the cloud-side inference with real-time video streaming, to achieve both low latency and high inference accuracy. The core idea is to send the video stream in low resolution, but recover the high-resolution frames from the low-resolution stream via a super-resolution procedure tailored for the actual analytics tasks. In essence, CloudSeg trades additional cloud-side computation (super-resolution) for significantly reduced network bandwidth. Our initial evaluation shows that compared to previous work, CloudSeg can reduce bandwidth consumption by ∼6.8× with negligible drop in accuracy.