Proceedings Article10.1109/ICMI.2002.1166960
Layered representations for human activity recognition
Nuria Oliver,Eric Horvitz,Ashutosh Garg +2 more
- 14 Oct 2002
- pp 3-8
TL;DR: The use of representation in a system that diagnoses states of a user's activity based on real-time streams of evidence from video, acoustic, and computer interactions is described.
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Abstract: We present the use of layered probabilistic representations using Hidden Markov Models for performing sensing, learning, and inference at multiple levels of temporal granularity. We describe the use of the representation in a system that diagnoses states of a user's activity based on real-time streams of evidence from video, acoustic, and computer interactions. We review the representation, present an implementation, and report on experiments with the layered representation in an office-awareness application.
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Citations
Human activity analysis: A review
Jake K. Aggarwal,Michael S. Ryoo +1 more
TL;DR: This article provides a detailed overview of various state-of-the-art research papers on human activity recognition, discussing both the methodologies developed for simple human actions and those for high-level activities.
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Discovery of activity patterns using topic models
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- 21 Sep 2008
TL;DR: Experimental results show the ability of the approach to model and recognize daily routines without user annotation to be able to be used in this work.
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Jonathan Lester,Tanzeem Choudhury,Nicky Kern,Gaetano Borriello,Blake Hannaford +4 more
- 30 Jul 2005
TL;DR: A hybrid approach to recognizing activities is presented, which combines boosting to discriminatively select useful features and learn an ensemble of static classifiers to recognize different activities, with hidden Markov models (HMMs) to capture the temporal regularities and smoothness of activities.
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TL;DR: This paper provides an overview of benchmark databases for activity recognition, the market analysis of video surveillance, and future directions to work on for this application.
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Advanced internet of things for personalised healthcare systems
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