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2016 IEEE Second International Conference on Multimedia Big Data (BigMM) (2016)
Taipei, Taiwan
April 20, 2016 to April 22, 2016
ISBN: 978-1-5090-2180-2
pp: 346-353
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.
INDEX TERMS
Metadata, Feature extraction, Three-dimensional displays, Cameras, Computational modeling, Bit rate, Monitoring
CITATION
Shannon Chen, Aahdar Jain, Zhenhuan Gao, Klara Nahrstedt, Ahsan Arefin, Raoul Rivas, "Metadata-Based Activity Analysis in 3D Tele-Immersion", 2016 IEEE Second International Conference on Multimedia Big Data (BigMM), vol. 00, no. , pp. 346-353, 2016, doi:10.1109/BigMM.2016.43
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