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Li, Yangfan, Ren, Wei, Zhu, Tianqing, Ren, Yi 
ORCID: https://orcid.org/0000-0001-7423-6719, Qin, Yue and Jie, Wei
(2018)
RIMS: A Real-time and intelligent monitoring system for live-broadcasting platforms.
Future Generation Computer Systems, 87.
pp. 259-266.
ISSN 0167-739X
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Abstract
Personal live shows on Internet streaming platforms currently are blooming as one of the most popular applications on mobile phones and especially attracting millions of young generation users. The content supervision on live streaming platforms, in which there are thousands or hundreds of show rooms for performing and chatting synchronously, is a major concern with the development of this new service. Traditional image captures and real-time content analysis experience huge difficulties such as processing delay, data overwhelming, and matching overhead. In this paper, we propose a comprehensive method to monitor real-time live stream and to identify illegal or unchartered live misbehaviors intelligently based on various proposed aspects instead of image analysis only. The proposed system called RIMS makes use of several novel indicators on show room status rather than analyzing images solely to support real-time requirements. Three detecting techniques are adopted: self-adaptive threshold-based abnormal traffic detection, sensitive Danmaku comment perception, and frame difference analysis. RIMS can detect dramatically increasing of user number in a show room, filter sensitive words in Danmaku, and capture segmentation of video scenes by frame difference analysis. We deploy our system to monitor a typical live- broadcasting platform called panda.tv, and overall accuracy of detection via three indicators reaches 90.1%. The application of RIMS can change current supervison methods on live platforms that they totally rely on real-time manual review or after the event check. The key techniques in RIMS can also be widely employed in many other mobile applications in edge computing such as video surveillance in Internet of Things and mobile short video sharing.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | live streaming platform,anomaly detection,fuzzy matching,frame difference analysis,state awareness |
| Faculty \ School: | Faculty of Science > School of Computing Sciences |
| UEA Research Groups: | Faculty of Science > Research Groups > Smart Emerging Technologies Faculty of Science > Research Groups > Data Science and AI |
| Related URLs: | |
| Depositing User: | Pure Connector |
| Date Deposited: | 26 Apr 2018 13:31 |
| Last Modified: | 10 Dec 2024 01:31 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/66842 |
| DOI: | 10.1016/j.future.2018.04.012 |
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