CloudEye: A New Paradigm of Video Analysis System for Mobile Visual Scenarios
Huan Cui (1, 2), Qing Li (3), Hanling Wang (1), Yong jiang (1) ((1), Tsinghua University, (2) Peking University, (3) Peng Cheng Laboratory)

TL;DR
CloudEye is a real-time mobile visual perception system that leverages edge and cloud computing to significantly reduce bandwidth, increase inference speed, and improve detection accuracy in resource-constrained environments.
Contribution
It introduces a novel system architecture with modules for fast inference, feature mining, and quality encoding tailored for mobile vision scenarios.
Findings
Reduces network bandwidth by 69.50%
Increases inference speed by 24.55%
Improves detection accuracy by 67.30%
Abstract
Mobile deep vision systems play a vital role in numerous scenarios. However, deep learning applications in mobile vision scenarios face problems such as tight computing resources. With the development of edge computing, the architecture of edge clouds has mitigated some of the issues related to limited computing resources. However, it has introduced increased latency. To address these challenges, we designed CloudEye which consists of Fast Inference Module, Feature Mining Module and Quality Encode Module. CloudEye is a real-time, efficient mobile visual perception system that leverages content information mining on edge servers in a mobile vision system environment equipped with edge servers and coordinated with cloud servers. Proven by sufficient experiments, we develop a prototype system that reduces network bandwidth usage by 69.50%, increases inference speed by 24.55%, and improves…
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Taxonomy
TopicsVideo Analysis and Summarization · Image Retrieval and Classification Techniques · Multimedia Communication and Technology
MethodsSPEED: Separable Pyramidal Pooling EncodEr-Decoder for Real-Time Monocular Depth Estimation on Low-Resource Settings
