论文标题
通过卷积共享在边缘启用协作视频感测
Enabling Collaborative Video Sensing at the Edge through Convolutional Sharing
论文作者
论文摘要
尽管深度神经网络（DNN）模型在机器视觉功能方面取得了显着进步，但它们的高计算复杂性和模型尺寸却呈现出强大的障碍，可以在基于Aiot的传感应用程序中部署。在本文中，我们提出了一种新颖的范式，网络中的同伴节点可以协作以提高他们对人检测的准确性，这是一项示例机器视觉任务。所提出的方法不需要重新训练DNN，并且会导致最小的处理延迟，因为它从合作者中提取场景摘要，并将其注入后，并将其注入参考摄像机的DNN。早期的结果显示出希望在基准数据集上与单个合作者的回忆提高10％的前景。
While Deep Neural Network (DNN) models have provided remarkable advances in machine vision capabilities, their high computational complexity and model sizes present a formidable roadblock to deployment in AIoT-based sensing applications. In this paper, we propose a novel paradigm by which peer nodes in a network can collaborate to improve their accuracy on person detection, an exemplar machine vision task. The proposed methodology requires no re-training of the DNNs and incurs minimal processing latency as it extracts scene summaries from the collaborators and injects back into DNNs of the reference cameras, on-the-fly. Early results show promise with improvements in recall as high as 10% with a single collaborator, on benchmark datasets.
