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Multi-client traffic video analysis task offloading decision 多端交通视频分析任务卸载决策

Lookup NU author(s): Dr Bin Qian

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Abstract

© 2024 Northeast University. All rights reserved.To address the problem of heavy computational tasks and uneven resource utilization among devices in multi-point monitoring video analysis in intelligent transportation, a cloud-controlled video analysis offloading scheme is proposed. Firstly, for the problem of insufficient client computing power to complete video analysis tasks, a video offloading framework is used to segment and offload some of the video analysis tasks to cloud servers. Then, a stage-optimized offloading algorithm is proposed to balance the load between servers and multiple clients, and improve resource utilization. Finally, to address the issue of different client requirements at different points, precision and energy consumption preferences are added to the algorithm to meet the needs of different clients. Experimental comparisons with other offloading schemes demonstrate that this scheme can better allocate video analysis tasks, improve system benefits, and the scalability of the system is demonstrated through extended experiments.

Publication metadata

Author(s): Wen Z-Y, Hu H-F, Qian B, Hong Z, Yu L

Publication type: Article

Publication status: Published

Journal: Kongzhi yu Juece/Control and Decision

Year: 2024

Volume: 39

Issue: 8

Pages: 2773-2782

Print publication date: 01/08/2024

Online publication date: 25/07/2024

Acceptance date: 02/04/2024

ISSN (print): 1001-0920

Publisher: Northeast University

URL: https://link.oversea.cnki.net/doi/10.13195/j.kzyjc.2023.0241

DOI: 10.13195/j.kzyjc.2023.0241

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