AI Oriented Large-Scale Video Management for Smart City: Technologies, Standards and Beyond

TL;DR

This paper discusses the challenges and solutions for large-scale video data management in smart cities, emphasizing deep feature coding and the need for standardization to enable effective AI-driven video analysis.

Contribution

It envisions a future standard for deep feature coding in AI-oriented large-scale video management and analyzes current techniques, standards, and open problems.

Findings

Deep feature coding offers a practical solution for large-scale video data management.

Standardization is crucial for interoperability in AI-driven video analysis.

Several open problems remain in the standardization process due to algorithm explosion and feature coding complexities.

Abstract

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.