具有成本效益的边缘检测驱动视频分析处理

Proceedings of the 3rd ACM Workshop on Hot Topics in Video Analytics and Intelligent Edges Pub Date : 2021-10-25 DOI:10.1145/3477083.3480156

Md. Adnan Arefeen, M. Y. S. Uddin

{"title":"具有成本效益的边缘检测驱动视频分析处理","authors":"Md. Adnan Arefeen, M. Y. S. Uddin","doi":"10.1145/3477083.3480156","DOIUrl":null,"url":null,"abstract":"We demonstrate a real-time video analytics system for applications that use objection detection models on incoming frames as part of their computation pipeline. Through edge-cloud collaboration, we show how a reinforcement learning based agent can skip successive video frames while keeping the object detection results almost intact for end applications.","PeriodicalId":206784,"journal":{"name":"Proceedings of the 3rd ACM Workshop on Hot Topics in Video Analytics and Intelligent Edges","volume":"12 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Cost effective processing of detection-driven video analytics at the edge\",\"authors\":\"Md. Adnan Arefeen, M. Y. S. Uddin\",\"doi\":\"10.1145/3477083.3480156\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We demonstrate a real-time video analytics system for applications that use objection detection models on incoming frames as part of their computation pipeline. Through edge-cloud collaboration, we show how a reinforcement learning based agent can skip successive video frames while keeping the object detection results almost intact for end applications.\",\"PeriodicalId\":206784,\"journal\":{\"name\":\"Proceedings of the 3rd ACM Workshop on Hot Topics in Video Analytics and Intelligent Edges\",\"volume\":\"12 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-10-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 3rd ACM Workshop on Hot Topics in Video Analytics and Intelligent Edges\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3477083.3480156\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 3rd ACM Workshop on Hot Topics in Video Analytics and Intelligent Edges","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3477083.3480156","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 0

摘要

我们演示了一个实时视频分析系统，该系统用于在传入帧上使用目标检测模型作为其计算管道的一部分。通过边缘云协作，我们展示了基于强化学习的智能体如何跳过连续的视频帧，同时保持最终应用的目标检测结果几乎完整。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Cost effective processing of detection-driven video analytics at the edge

We demonstrate a real-time video analytics system for applications that use objection detection models on incoming frames as part of their computation pipeline. Through edge-cloud collaboration, we show how a reinforcement learning based agent can skip successive video frames while keeping the object detection results almost intact for end applications.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Proceedings of the 3rd ACM Workshop on Hot Topics in Video Analytics and Intelligent Edges

自引率

0.00%

发文量