让边缘设备实时分析可转动高清摄像头视频，精度提升45%。
Uirapuru: Timely Video Analytics for High-Resolution Steerable Cameras on Edge Devices
- 根据镜头运动动态调整画面分块策略，提升适应性。
- 在延迟约束下精度最高提升45%，推理速度最高快4.53倍。
- 适合智能交通、人流监控等需要转动摄像头的场景。
高分辨率摄像头的实时视频分析已成为交通管控、人群监测等智能服务的重要技术。现有方法虽能保证精度与延迟，但主要针对固定视角摄像头。本文提出Uirapuru框架，实现基于边缘设备的高分辨率可转动摄像头实时分析。由于镜头动作带来场景剧烈变化，传统帧切片方法效果不佳。Uirapuru将镜头运动信息融入系统设计，并在每帧上快速自适应分块。我们在包含典型云台-俯仰-变焦（PTZ）运动的高分辨率视频数据集及真实PTZ摄像头采集视频上评估，结果表明：相比最先进静态相机方法，在满足指定延迟预算前提下，精度最高提升1.45倍；或在精度相当情况下，推理速度最高提升4.53倍。
原文摘要 · Abstract (English)
Real-time video analytics on high-resolution cameras has become a popular technology for various intelligent services like traffic control and crowd monitoring. While extensive work has been done on improving analytics accuracy with timing guarantees, virtually all of them target static viewpoint cameras. In this paper, we present Uirapuru, a novel framework for real-time, edge-based video analytics on high-resolution steerable cameras. The actuation performed by those cameras brings significant dynamism to the scene, presenting a critical challenge to existing popular approaches such as frame tiling. To address this problem, Uirapuru incorporates a comprehensive understanding of camera actuation into the system design paired with fast adaptive tiling at a per-frame level. We evaluate Uirapuru on a high-resolution video dataset, augmented by pan-tilt-zoom (PTZ) movements typical for steerable cameras and on real-world videos collected from an actual PTZ camera. Our experimental results show that Uirapuru provides up to 1.45x improvement in accuracy while respecting specified latency budgets or reaches up to 4.53x inference speedup with on-par accuracy compared to state-of-the-art static camera approaches.
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