AI Oriented Large-Scale Video Management for Smart City: Technologies, Standards and Beyond
This work tackles the problem of efficient video management for smart city applications, but it is incremental as it builds on existing deep learning and feature coding techniques without introducing a new method.
The paper addresses the challenge of managing large-scale video data for smart city surveillance by proposing deep feature coding as a solution, and it discusses the need for standardization to enable interoperability despite existing problems in the process.
Deep learning has achieved substantial success in a series of tasks in computer vision. Intelligent video analysis, which can be broadly applied to video surveillance in various smart city applications, can also be driven by such powerful deep learning engines. To practically facilitate deep neural network models in the large-scale video analysis, there are still unprecedented challenges for the large-scale video data management. Deep feature coding, instead of video coding, provides a practical solution for handling the large-scale video surveillance data. To enable interoperability in the context of deep feature coding, standardization is urgent and important. However, due to the explosion of deep learning algorithms and the particularity of feature coding, there are numerous remaining problems in the standardization process. This paper envisions the future deep feature coding standard for the AI oriented large-scale video management, and discusses existing techniques, standards and possible solutions for these open problems.