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S-SEER: A Multimodal Office Activity Recognition System with Selective Perception
This video was recorded at Joint AMI/PASCAL/IM2/M4 Workshop on Multimodal Interaction and Related Machine Learning Algorithms, Martigny 2004. 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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This work is licensed under a Attribution-NonCommercial-NoDerivs 3.0 United States
