Title: Visual Activity Analysis for Geriatric Medicine
Dr. Alexander Hauptmann, School of Computer Science, Carnegie Mellon University
Date: March 4, 2011
Time: 10:20 am
Room: 1279 Anthony Hall
Analyzing human activity is the key to understanding and searching surveillance videos. I will discuss the current results from a study to utilize automatic
human activities analysis over longer terms to improve health care in a nursing home setting. Beyond just recognizing human activities observed
on video, we analyze a 25 day archive of observations in a nursing home, and link the observational results to medical records. This work explores the statistical patterns between a patient's daily activity and his/her clinical diagnosis.
Our main contribution is in developing and using an intelligent visual surveillance system based on efficient and robust activity analysis and a demonstration of the feasibility of exploiting long term human activity patterns though video analysis.
Alex Hauptmann received his B.A. and M.A. in Psychology from Johns Hopkins University, studied Computer Science at the Technische Universität Berlin from 1982-1984, and received his Ph.D. in Computer Science from Carnegie Mellon University in 1991. He is currently on the faculty in the Computer Science Department and the Language Technologies Institute at CMU. His research interests have led him to pursue and combine several different areas: man-machine communication, natural language processing, speech understanding and synthesis, video analysis and machine learning. He worked on speech and machine translation from 1984-94, when he joined the Informedia project for digital video analysis and retrieval and led the development and evaluation of the News-on-Demand applications.