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MoVi: mobile phone based video highlights via collaborative sensing

Authors:

Romit Roy ChoudhuryAuthors Info & Claims

MobiSys '10: Proceedings of the 8th international conference on Mobile systems, applications, and services

Pages 357 - 370

https://doi.org/10.1145/1814433.1814468

Published: 15 June 2010 Publication History

Abstract

Sensor networks have been conventionally defined as a network of sensor motes that collaboratively detect events and report them to a remote monitoring station. This paper makes an attempt to extend this notion to the social context by using mobile phones as replacement of motes. We envision a social application where mobile phones collaboratively sense their ambience and recognize socially "interesting" events. The phone with a good view of the event triggers a video recording, and later, the video-clips from different phones are "stitched" into a video highlights of the occasion. We observe that such a video highlights is akin to the notion of event coverage in conventional sensor networks, only the notion of "event" has changed from physical to social. We have built a Mobile Phone based Video Highlights system (MoVi) using Nokia phones and iPod Nanos, and have experimented in real-life social gatherings. Results show that MoVi-generated video highlights (created offline) are quite similar to those created manually, (i.e., by painstakingly editing the entire video of the occasion). In that sense, MoVi can be viewed as a collaborative information distillation tool capable of filtering events of social relevance.

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Cited By

Zhong WChen HPan ZZheng CWang A(2023)COSense: collaborative and opportunistic sensing of road events by vehicles’ camerasCCF Transactions on Pervasive Computing and Interaction10.1007/s42486-023-00126-95:3(276-287)Online publication date: 15-Feb-2023
https://doi.org/10.1007/s42486-023-00126-9
Yi JMin CKawsar F(2021)Vision PaperProceedings of the 19th ACM Conference on Embedded Networked Sensor Systems10.1145/3485730.3493453(474-477)Online publication date: 15-Nov-2021
https://dl.acm.org/doi/10.1145/3485730.3493453
Yang JWang CJiang BSong HMeng Q(2021)Visual Perception Enabled Industry Intelligence: State of the Art, Challenges and ProspectsIEEE Transactions on Industrial Informatics10.1109/TII.2020.299881817:3(2204-2219)Online publication date: Mar-2021
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Index Terms

MoVi: mobile phone based video highlights via collaborative sensing

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Published In

cover image ACM Conferences

MobiSys '10: Proceedings of the 8th international conference on Mobile systems, applications, and services

June 2010

382 pages

ISBN:9781605589855

DOI:10.1145/1814433

General Chair:
Sujata Banerjee
Hewlett-Packard Labs., USA
,
Program Chairs:
Srinivasan Keshav
University of Waterloo, Ontario, Canada
,
Alec Wolman
Microsoft Research Redmond, USA

Copyright © 2010 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGMOBILE: ACM Special Interest Group on Mobility of Systems, Users, Data and Computing

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SIGOPS: ACM Special Interest Group on Operating Systems

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Association for Computing Machinery

New York, NY, United States

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Published: 15 June 2010

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MobiSys'10

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SIGMOBILE

MobiSys'10: The 8th International Conference on Mobile Systems, Applications, and Services

June 15 - 18, 2010

California, San Francisco, USA

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Overall Acceptance Rate 274 of 1,679 submissions, 16%

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Cited By

Zhong WChen HPan ZZheng CWang A(2023)COSense: collaborative and opportunistic sensing of road events by vehicles’ camerasCCF Transactions on Pervasive Computing and Interaction10.1007/s42486-023-00126-95:3(276-287)Online publication date: 15-Feb-2023
https://doi.org/10.1007/s42486-023-00126-9
Yi JMin CKawsar F(2021)Vision PaperProceedings of the 19th ACM Conference on Embedded Networked Sensor Systems10.1145/3485730.3493453(474-477)Online publication date: 15-Nov-2021
https://dl.acm.org/doi/10.1145/3485730.3493453
Yang JWang CJiang BSong HMeng Q(2021)Visual Perception Enabled Industry Intelligence: State of the Art, Challenges and ProspectsIEEE Transactions on Industrial Informatics10.1109/TII.2020.299881817:3(2204-2219)Online publication date: Mar-2021
https://doi.org/10.1109/TII.2020.2998818
Yu ZWang ZYu ZWang Z(2020)Group Behavior RecognitionHuman Behavior Analysis: Sensing and Understanding10.1007/978-981-15-2109-6_6(139-218)Online publication date: 1-Mar-2020
https://doi.org/10.1007/978-981-15-2109-6_6
Tan WJiang ZTavares JXu Z(2019)A novel experience-based incentive mechanism for mobile crowdsensing systemProceedings of the International Conference on Artificial Intelligence, Information Processing and Cloud Computing10.1145/3371425.3371459(1-6)Online publication date: 19-Dec-2019
https://dl.acm.org/doi/10.1145/3371425.3371459
Yang ZYu CZheng FShi Y(2019)ProxiTalkProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/33512763:3(1-25)Online publication date: 9-Sep-2019
https://dl.acm.org/doi/10.1145/3351276
Pore MChakati VBanerjee AGupta S(2019)ContextAiDeACM Transactions on Internet Technology10.1145/330144419:2(1-23)Online publication date: 3-Apr-2019
https://dl.acm.org/doi/10.1145/3301444
Wang LYang CYu ZLiu YWang ZGuo B(2019)CrackSense: A CrowdSourcing Based Urban Road Crack Detection System2019 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI)10.1109/SmartWorld-UIC-ATC-SCALCOM-IOP-SCI.2019.00188(944-951)Online publication date: Aug-2019
https://doi.org/10.1109/SmartWorld-UIC-ATC-SCALCOM-IOP-SCI.2019.00188
Capponi AFiandrino CKantarci BFoschini LKliazovich DBouvry P(2019)A Survey on Mobile Crowdsensing Systems: Challenges, Solutions, and OpportunitiesIEEE Communications Surveys & Tutorials10.1109/COMST.2019.291403021:3(2419-2465)Online publication date: Nov-2020
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Liu TZhang YYang XTong W(2019)Near-Optimal User Recruitment in Mobile Crowdsensing for Urban Fine-Grained Event DetectionIEEE Access10.1109/ACCESS.2019.2961384(1-1)Online publication date: 2019
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