3D teleimmersive activity classification based on application-system metadata

Authors:
Aadhar Jain;Ahsan Arefin;Raoul Rivas;Chien-nan Chen;Klara Nahrstedt
Affiliations:
University Of Illinois, Urbana Champaign, Champaign, USA;University Of Illinois, Urbana Champaign, Champaign, USA;University Of Illinois, Urbana Champaign, Champaign, USA;University Of Illinois, Urbana Champaign, Champaign, USA;University Of Illinois, Urbana Champaign, Champaign, USA
Venue:
Proceedings of the 21st ACM international conference on Multimedia
Year:
2013

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Abstract

Being able to detect and recognize human activities is essential for 3D collaborative applications for efficient quality of service provisioning and device management. A broad range of research has been devoted to analyze media data to identify human activity, which requires the knowledge of data format, application-specific coding technique and computationally expensive image analysis. In this paper, we propose a human activity detection technique based on application generated metadata and related system metadata. Our approach does not depend on specific data format or coding technique. We evaluate our algorithm with different cyber-physical setups, and show that we can achieve very high accuracy (above 97%) by using a good learning model.