Distributed cameras have been used widely for real-time image recognition. There are two main approaches in distributed camera systems: (1) The cameras are equipped with a powerful high-end processor for local image processing, (2) Low-cost cameras with resource-constrained processors are used for capturing the images and transferring them to a cloud server for classification purposes. The first approach is costly and not scalable. The second approach is too slow for real-time object detection due to the transfer delays to a remote server. These problems exacerbate in multi-view image recognition, where a central platform is required for collective image processing of multiple images from the same scene. For this purpose, typically, a cloud server is used, which does not meet real-time recognition, network bandwidth, scalability and power consumption constraints of such systems. This paper proposes hierarchical neural network structures that can be realized in an edge orchestration architecture at different levels, i.e., cameras, edge devices, and cloud servers to enable deep learning capabilities for real-time multi-view image recognition. This would enable us to detect objects in the proximity of multiple cameras and transfer data for deeper layer processing to the cloud server and training of all connected edge devices and cameras.

中文翻译：

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基于边缘编排架构的分布式智能相机系统

分布式相机已广泛用于实时图像识别。分布式相机系统有两种主要方法：（1）相机配备强大的高端处理器用于本地图像处理，（2）具有资源受限处理器的低成本相机用于捕获图像并传输图像到云服务器进行分类。第一种方法成本高且不可扩展。由于到远程服务器的传输延迟，第二种方法对于实时对象检测来说太慢了。这些问题在多视图图像识别中加剧，其中需要一个中央平台来对来自同一场景的多个图像进行集体图像处理。为此，通常使用云服务器，不满足实时识别、网络带宽、此类系统的可扩展性和功耗限制。本文提出了层次神经网络结构，可以在不同层次的边缘编排架构中实现，即相机、边缘设备和云服务器，以实现实时多视图图像识别的深度学习能力。这将使我们能够检测多个摄像头附近的物体，并将数据传输到云服务器进行更深层次的处理，并训练所有连接的边缘设备和摄像头。