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AIC Seminar Series

Stochastic Models for Learning and Recognizing Human Activities

Hung H BuiAritificial Intelligence Center, SRI International[Home Page]

Date:  2004-10-14 at 16:00

Location:  EJ228  (Directions)

   Abstract

Understanding the typical patterns in the user’s daily activities and routines from low-level sensory data is an important problem in many applications such as smart environment and intelligent assistance technology. Due to the sequential nature that activities unfold in time, dynamic models such as the Hidden Markov Model (HMM) has been widely used for activity learning and classification. However, as the space of activities becomes more complex, other structural properties need to be exploited, including the hierarchical organization and duration of activities. In this talk, I will describe several extensions of the basic HMM for this modeling task, including the Abstract HMM (AHMM), Hierarchical HMM (HHMM) and Hidden Semi-Markov Model (HSMM). Experiments in the camera-surveillance domain demonstrate the advantages of these models over the basic HMM.

   Bio for Hung H Bui

Hung Bui is a computer scientist at the AI Center, SRI International. Prior to joining SRI, he was an assistant professor at the Department of Computer Science, Curtin University of Technology in Perth, Australia. His research interests include probabilistic reasoning, statistical machine learning, graphical models, and applications to plan and activity recognition.

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