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Monday, July 8 • 4:15pm - 4:30pm

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 {\em 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 {\em 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 $\sim$6.8$\times$ with negligible drop in accuracy.

Speakers
YW

HKUST
WW

HKUST
JZ

HKUST
JJ

## Junchen Jiang

University of Chicago
KC

## Kai Chen

HKUST

Monday July 8, 2019 4:15pm - 4:30pm PDT
HotCloud: Grand Ballroom VII–IX