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

Lingyu Duan; Yihang Lou; Shiqi Wang; Wen Gao; Yong Rui

doi:10.1109/MMUL.2018.2873564

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

Lingyu Duan
, Yihang Lou
, Shiqi Wang
, Wen Gao
, Yong Rui

Peking University
City University of Hong Kong
Lenovo

Research output: Contribution to journal › Article › peer-review

46 Scopus citations

Abstract

Deep learning has achieved substantial success in intelligent video analysis. To practically facilitate deep neural network models in the large-scale video analysis, there are still unprecedented challenges. 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. 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.

Original language	English
Article number	8509149
Pages (from-to)	8-20
Number of pages	13
Journal	IEEE Multimedia
Volume	26
Issue number	2
DOIs	https://doi.org/10.1109/MMUL.2018.2873564
State	Published - 1 Apr 2019
Externally published	Yes

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 11 Sustainable Cities and Communities

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10.1109/MMUL.2018.2873564

Cite this

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abstract = "Deep learning has achieved substantial success in intelligent video analysis. To practically facilitate deep neural network models in the large-scale video analysis, there are still unprecedented challenges. 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. 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.",

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AB - Deep learning has achieved substantial success in intelligent video analysis. To practically facilitate deep neural network models in the large-scale video analysis, there are still unprecedented challenges. 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. 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.

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

Abstract

UN SDGs

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