{"title":"联合移动云视频稳定","authors":"G. S. Adesoye, Oliver Wang","doi":"10.1109/CVPRW.2017.49","DOIUrl":null,"url":null,"abstract":"In this work we analyze the complex trade-off between data transfer, computation time, and power consumption when a multi-stage data-intensive algorithm (in this case video stabilization) is split between a low power mobile device and high power cloud server. We evaluate design choices in terms of which intermediate representations should be transferred to the server and back to the mobile device, and present a graph-based solution that can update the optimal joint mobile-cloud computation separation as the hardware configuration or user's requirements change. The practices we employ in this work can be extended to other mobile computer vision applications.","PeriodicalId":6668,"journal":{"name":"2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)","volume":"61 1","pages":"353-360"},"PeriodicalIF":0.0000,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Joint Mobile-Cloud Video Stabilization\",\"authors\":\"G. S. Adesoye, Oliver Wang\",\"doi\":\"10.1109/CVPRW.2017.49\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this work we analyze the complex trade-off between data transfer, computation time, and power consumption when a multi-stage data-intensive algorithm (in this case video stabilization) is split between a low power mobile device and high power cloud server. We evaluate design choices in terms of which intermediate representations should be transferred to the server and back to the mobile device, and present a graph-based solution that can update the optimal joint mobile-cloud computation separation as the hardware configuration or user's requirements change. The practices we employ in this work can be extended to other mobile computer vision applications.\",\"PeriodicalId\":6668,\"journal\":{\"name\":\"2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)\",\"volume\":\"61 1\",\"pages\":\"353-360\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CVPRW.2017.49\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CVPRW.2017.49","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
In this work we analyze the complex trade-off between data transfer, computation time, and power consumption when a multi-stage data-intensive algorithm (in this case video stabilization) is split between a low power mobile device and high power cloud server. We evaluate design choices in terms of which intermediate representations should be transferred to the server and back to the mobile device, and present a graph-based solution that can update the optimal joint mobile-cloud computation separation as the hardware configuration or user's requirements change. The practices we employ in this work can be extended to other mobile computer vision applications.