S-SEER: A Multimodal Office Activity Recognition System with Selective Perception
published: Feb. 25, 2007, recorded: June 2004, views: 267
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I will present the use of layered probabilistic representations for modeling the activities of people in a system named S-SEER. I will describe how we use the representation to do sensing, learning and inference at multiple levels of temporal granularity and abstraction. The approach centers on the use of a cascade of Hidden Markov Models (HMMs) 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 (keyboard and mouse) interactions.
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