为机器用的事件相机视频设计低延迟流传输方案
Scalable Event-Based Video Streaming for Machines with MoQ
- 基于Media Over QUIC协议新版本，设计事件视频专用流格式
- 支持可扩展事件数据传输，降低端到端延迟
- 适合实时计算机视觉系统，如自动驾驶、机器人导航
有损压缩和码率自适应流传输是传统视频流的核心。然而，一类新型类脑「事件」传感器以异步像素采样方式记录视频，而非图像帧。这些传感器专为计算机视觉应用设计，而非人类观看。迄今研究主要聚焦于应用开发，忽视了数据传输这一关键问题。本文调研了事件视频系统现状，讨论了近期可扩展事件流传输工作的技术挑战，并提出一种基于最新媒体过QUIC协议草案的新低延迟事件视频流格式。
原文摘要 · Abstract (English)
Lossy compression and rate-adaptive streaming are a mainstay in traditional video steams. However, a new class of neuromorphic ``event'' sensors records video with asynchronous pixel samples rather than image frames. These sensors are designed for computer vision applications, rather than human video consumption. Until now, researchers have focused their efforts primarily on application development, ignoring the crucial problem of data transmission. We survey the landscape of event-based video systems, discuss the technical issues with our recent scalable event streaming work, and propose a new low-latency event streaming format based on the latest additions to the Media Over QUIC protocol draft.
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