The advent of edge computing has made real-time intelligent video analytics feasible. Previous works, based on traditional model architecture (e.g., CNN, RNN, etc.), employ various strategies to filter out non-region-of-interest content to minimize bandwidth and computation consumption but show inferior performance in adverse environments. Recently, visual foundation models based on transformers have shown great performance in adverse environments due to their amazing generalization capability. However, they require a large amount of computation power, which limits their applications in real-time intelligent video analytics. In this paper, we find visual foundation models like Vision Transformer (ViT) also have a dedicated acceleration mechanism for video analytics. To this end, we introduce Arena, an end-to-end edge-assisted video inference acceleration system based on ViT. We leverage the capability of ViT that can be accelerated through token pruning by only offloading and feeding Patches-of-Interest (PoIs) to the downstream models. Additionally, we employ probability-based patch sampling, which provides a simple but efficient mechanism for determining PoIs where the probable locations of objects are in subsequent frames. Through extensive evaluations on public datasets, our findings reveal that Arena can boost inference speeds by up to $1.58\times$ and $1.82\times$ on average while consuming only 54% and 34% of the bandwidth, respectively, all with high inference accuracy.

通过引入 Arena，基于 Vision Transformer（ViT）的端到端边缘辅助视频推理加速系统，利用 ViT 的能力通过令牌修剪加速，仅将感兴趣区域（PoIs）卸载并传递给下游模型，还使用基于概率的补丁抽样确定连续帧中对象的可能位置，通过在公共数据集上广泛评估，结果表明 Arena 可以提高推理速度达到平均 1.58 倍和 1.82 倍，同时只消耗带宽的 54% 和 34%，而推理准确性也很高。

Arena: 对边缘辅助视频分析的感兴趣区域可见光成像推断加速系统