Proceedings of the Workshop on Smart Internet of Things最新文献

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Mining and analysis of public information for insight into personal fitness tracker reliability, operations and user performance 挖掘和分析公共信息，深入了解个人健身追踪器的可靠性、运营和用户表现

Proceedings of the Workshop on Smart Internet of Things Pub Date : 2017-10-14 DOI: 10.1145/3132479.3132486

Nancy Carter, Qun A. Li, Jiquo Yu

引用次数: 0

VU: video usefulness and its application in large-scale video surveillance systems: an early experience VU:视频有用性及其在大规模视频监控系统中的应用:早期经验

Proceedings of the Workshop on Smart Internet of Things Pub Date : 2017-10-14 DOI: 10.1145/3132479.3132485

Hui Sun, Xu Liang, Weisong Shi

{"title":"VU: video usefulness and its application in large-scale video surveillance systems: an early experience","authors":"Hui Sun, Xu Liang, Weisong Shi","doi":"10.1145/3132479.3132485","DOIUrl":"https://doi.org/10.1145/3132479.3132485","url":null,"abstract":"In the era of smart and connected communities, a video surveillance system, which usually involves tens and thousands of video cameras, has increasingly become a prominent component for the public safety. In the current practice, when the video surveillance system has a failure, the operation and maintenance team usually spends a lot of time to identify and locate the failure, which cannot guarantee real-time in a large-scale video surveillance system. Meanwhile, the video data with a failure wastes amount of storage space in the cloud. The emergence of edge computing is very promising in the preprocessing for source video data at an edge camera, and video surveillance systems are one of the popular applications for edge computing. In this paper, we propose VU, a Video Usefulness model for large-scale video surveillance systems, and explore its application, such as early failure detection and storage saving. The VU model evaluates the usefulness of video data in a real-time fashion and notifies failures to end-users on the fly. This paper has three contributions: (1) a comprehensive video usefulness model has been proposed. To the best of our knowledge, this is the first work aiming to quality the video usefulness in a real application; (2) real-time failure detection algorithms based on edge computing and cloud computing are proposed to efficiently improve the mean time to repair (i.e., MTTR); (3) effective storage and bandwidth saving schemes for large-scale video surveillance systems are proposed and implemented. Results from a university-wide surveillance system consisting of 2,960 cameras show that failures of video data in different domains are accurately detected by VU model. MTTR is largely shortened by the fast detection algorithm in real time. The video data with the worst degree of VU is mostly discarded to reduce overload in the network and save storage space in the cloud.","PeriodicalId":446149,"journal":{"name":"Proceedings of the Workshop on Smart Internet of Things","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130790584","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 23

Proceedings of the Workshop on Smart Internet of Things 智能物联网研讨会论文集

Proceedings of the Workshop on Smart Internet of Things Pub Date : 1900-01-01 DOI: 10.1145/3132479

引用次数: 2

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