VPPlus: Exploring the Potentials of Video Processing for Live Video Analytics at the Edge

被引:1
|
作者
Guo, Junpeng [1 ]
Xia, Shengqing [1 ]
Peng, Chunyi [1 ]
机构
[1] Purdue Univ, Dept Comp Sci, W Lafayette, IN 47907 USA
来源
2022 IEEE/ACM 30TH INTERNATIONAL SYMPOSIUM ON QUALITY OF SERVICE (IWQOS) | 2022年
基金
美国国家科学基金会;
关键词
Video analytics; edge computing; on-device processing; video compression;
D O I
10.1109/IWQoS54832.2022.9812896
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
Edge-assisted video analytics is gaining momentum. In this work, we tackle an important problem to compress video content live streamed from the device to the edge without scarifying accuracy and timeliness of its video analytics. We find that on-device processing can be tuned over a larger configuration space for more video compression, which was largely overlooked. Inspired by our pilot study, we design VPPlus to fulfill the potentials to compress the video as much as we can, while preserving analytical accuracy. VPPlus incorporates two core modules - offline profiling and online adaptation - to generate proper feedback automatically and quickly to tune on-device processing. We validate the effectiveness and efficiency of VPPlususing five object detection tasks over two popular datasets; VPPlus outperforms the state-of-art approaches in almost all the cases.
引用
收藏
页数:11
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