Metadata-based activity analysis in 3D tele-immersion

Shannon Chen, Aahdar Jain, Zhenhuan Gao, Klara Nahrstedt, Ahsan Arefin, Raoul Rivas

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In view of the necessity of activity analysis on Gbps-scale 3D Tele-Immersive (3DTI) content bundle and petabyte-scale 3DTI recordings, this work proposes and verifies the feasibility of replacing high-latency intrusive analysis with light-weighted metadata-based analysis. For real-time in-session use case, result shows that metadata-based analysis module, when personalized with user's body index, can achieve accuracies in 90 percentile on classifying various activity classes. For offline cross-session use case, we propose a hybrid analysis scheme which combines the advantages of intrusive analysis (i.e., conventional activity analysis on content level) and metadata-based analysis and achieves high accuracy with low computation latency.

Original languageEnglish (US)
Title of host publicationProceedings - 2016 IEEE 2nd International Conference on Multimedia Big Data, BigMM 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages346-353
Number of pages8
ISBN (Electronic)9781509021789
DOIs
StatePublished - Aug 16 2016
Event2nd IEEE International Conference on Multimedia Big Data, BigMM 2016 - Taipei, Taiwan, Province of China
Duration: Apr 20 2016Apr 22 2016

Publication series

NameProceedings - 2016 IEEE 2nd International Conference on Multimedia Big Data, BigMM 2016

Other

Other2nd IEEE International Conference on Multimedia Big Data, BigMM 2016
CountryTaiwan, Province of China
CityTaipei
Period4/20/164/22/16

Keywords

  • 3DTI
  • Activity analysis
  • Metadata

ASJC Scopus subject areas

  • Signal Processing
  • Information Systems
  • Media Technology

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  • Cite this

    Chen, S., Jain, A., Gao, Z., Nahrstedt, K., Arefin, A., & Rivas, R. (2016). Metadata-based activity analysis in 3D tele-immersion. In Proceedings - 2016 IEEE 2nd International Conference on Multimedia Big Data, BigMM 2016 (pp. 346-353). [7545048] (Proceedings - 2016 IEEE 2nd International Conference on Multimedia Big Data, BigMM 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigMM.2016.43