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Abstract
En 中文
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 arc 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. So, there is a need for a scalable intelligent engine that can adapt itself based on the communication and processing delays and energy level of cameras (if battery operated) to accomplish real-time object recognition. This paper proposes to move from the traditional cognitive services based on training models in the cloud and requesting inference locally at cameras to a hierarchical bandwidth-efficient machine learning structure spread across cameras, edge devices and the cloud server 14 handling high density data streams.
Keywords:
Edge Computing
Internet of Things
Distributed Cameras
Journal
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Papers:
3
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