Layered representations for learning and inferring office activity from multiple sensory channels

@article{Oliver2004LayeredRF,
  title={Layered representations for learning and inferring office activity from multiple sensory channels},
  author={Nuria Oliver and Ashutosh Garg and Eric Horvitz},
  journal={Computer Vision and Image Understanding},
  year={2004},
  volume={96},
  pages={163-180}
}
We present the use of layered probabilistic representations for modeling human activities, and describe how we use the representation to do sensing, learning, and inference at multiple levels of temporal granularity and abstraction and from heterogeneous data sources. The approach centers on the use of a cascade of Hidden Markov Models named Layered Hidden Markov Models (LHMMs) to diagnose states of a user!s activity based on real-time streams of evidence from video, audio, and computer… CONTINUE READING
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