PETRI: Reducing Bandwidth Requirement in Smart Surveillance by Edge-Cloud Collaborative Adaptive Frame Clustering and Pipelined Bidirectional Tracking

Neural networks running on cloud servers have been widely used in smart surveillance, but they require high bandwidth to upload videos. Edge-cloud collaborative encoding based on ROI (Region-Of-Interest) can reduce bandwidth requirement, but it suffers from inaccurate ROI detection due to feedback l...

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Vydané v:	2021 58th ACM/IEEE Design Automation Conference (DAC) s. 421 - 426
Hlavní autori:	Liu, Ruoyang, Zhang, Lu, Wang, Jingyu, Yang, Huazhong, Liu, Yongpan
Médium:	Konferenčný príspevok..
Jazyk:	English
Vydavateľské údaje:	IEEE 05.12.2021
Predmet:	Bandwidth Collaboration collaborative architecture edge computing Image edge detection Object detection Pipelines Surveillance Target tracking
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