While large deep neural networks excel at general video analytics tasks, the significant demand on computing capacity makes them infeasible for real-time inference on resource-constrained end cam-eras. In this paper, we propose an edge-assisted framework that continuously updates the lightweight model deployed on the end cameras to achieve accurate predictions in adverse environments. This framework consists of three modules, namely, a key frame extractor, a trigger controller, and a retraining manager. The low-cost key frame extractor obtains frames that can best represent the current environment. Those frames are then transmitted and buffered as the retraining data for model update at the edge server. Once the trigger controller detects a significant accuracy drop in the selected frames, the retraining manager outputs the optimal retraining configuration balancing the accuracy and time cost. We prototype our system on two end devices of different computing capacities with one edge server. The results demonstrate that our approach significantly improves accuracy across all tested adverse environment scenarios (up to 24%) and reduces more than 50% of the retraining time compared to existing benchmarks.

翻译：虽然大型深度神经网络在通用视频分析任务中表现出色，但其对计算能力的巨大需求使其无法在资源受限的终端摄像头上实现实时推理。本文提出了一种边缘辅助框架，该框架持续更新部署在终端摄像头上的轻量级模型，以在恶劣环境中实现准确预测。该框架包含三个模块：关键帧提取器、触发控制器和重训练管理器。低成本的关键帧提取器获取最能代表当前环境的帧，这些帧随后被传输并缓存为边缘服务器上模型更新的重训练数据。一旦触发控制器检测到选定帧的准确率显著下降，重训练管理器将输出平衡准确率与时间成本的最优重训练配置。我们在一个边缘服务器与两台不同计算能力的终端设备上实现了系统原型。结果表明，与现有基准相比，我们的方法在所有测试的恶劣环境场景中均显著提升了准确率（最高达24%），并将重训练时间减少了50%以上。