S-SEER: A Multimodal Office Activity Recognition System with Selective Perception

Posted in Science on July 21, 2008


S-SEER: A Multimodal Office Activity Recognition System with Selective Perception

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.

Author: Nuria Oliver, Tel Aviv University

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Tags: Science, Lectures, Computer Science, Machine Learning, VideoLectures.Net, Markov Processes, Computer Vision