S-SEER: A Multimodal Office Activity Recognition System with Selective Perception
published: Feb. 25, 2007, recorded: June 2004, views: 6192
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
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.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !