High-definition (HD) cameras for surveillance and road traffic have experienced tremendous growth, demanding intensive computation resources for real-time analytics. Recently, offloading frames from the front-end device to the back-end edge server has shown great promise. In multi-stream competitive environments, efficient bandwidth management and proper scheduling are crucial to ensure both high inference accuracy and high throughput. To achieve this goal, we propose BiSwift, a bi-level framework that scales the concurrent real-time video analytics by a novel adaptive hybrid codec integrated with multi-level pipelines, and a global bandwidth controller for multiple video streams. The lower-level front-back-end collaborative mechanism (called adaptive hybrid codec) locally optimizes the accuracy and accelerates end-to-end video analytics for a single stream. The upper-level scheduler aims to accuracy fairness among multiple streams via the global bandwidth controller. The evaluation of BiSwift shows that BiSwift is able to real-time object detection on 9 streams with an edge device only equipped with an NVIDIA RTX3070 (8G) GPU. BiSwift improves 10%$\sim$21% accuracy and presents 1.2$\sim$9$\times$ throughput compared with the state-of-the-art video analytics pipelines.

翻译：高清监控与交通摄像头数量激增，其实时分析对计算资源提出了极高需求。最新研究将前端设备帧任务卸载至后端边缘服务器，展现出巨大潜力。在多流竞争环境下，高效带宽管理与合理调度对确保高推理精度与高吞吐量至关重要。为此，我们提出BiSwift双层级框架：下层通过集成多级流水线的自适应混合编解码器实现并发实时视频分析加速，上层通过全局带宽控制器实现多视频流调度。下层的前后端协同机制（自适应混合编解码器）可单流局部优化精度并加速端到端视频分析；上层的调度器通过全局带宽控制器实现多流间的精度公平性。实验表明，BiSwift仅需配备NVIDIA RTX3070（8G）GPU的边缘设备即可支持9路实时目标检测。相比现有最优视频分析流水线，BiSwift提升10%~21%精度，吞吐量提高1.2~9倍。