CloudEye通过分层计算提升移动端视觉系统实时性与效率。
CloudEye: A New Paradigm of Video Analysis System for Mobile Visual Scenarios
- 将推理、特征挖掘与质量编码分层部署于边缘与云端协同处理
- 网络带宽减少69.50%，推理速度提升24.55%，检测准确率提高67.30%
- 适合资源受限的移动视觉场景，如智能安防、车载视觉
移动端深度视觉系统在众多场景中至关重要，但受限于计算资源紧张。随着边缘计算发展，边缘云架构缓解了部分算力瓶颈，却引入了延迟增加的问题。为此，我们设计了CloudEye，包含快速推理模块、特征挖掘模块和质量编码模块。该系统是面向配备边缘服务器的移动视觉环境的实时高效感知方案，通过边缘服务器上的内容信息挖掘并协调云端实现优化。大量实验验证，原型系统可降低69.50%的网络带宽使用，提升24.55%的推理速度，并使检测准确率提高67.30%。
原文摘要 · Abstract (English)
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 detection accuracy by 67.30%
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