A Bayesian computer vision system for modeling human interactions
Oliver, N.M.
Rosario, B.
Pentland, A.P.
Adaptive Syst. & Interaction Group, Microsoft Corp., Redmond, WA;
This paper appears in: Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publication Date: Aug 2000
Volume: 22,
Issue: 8
On page(s): 831-843
ISSN: 0162-8828
References Cited: 34
CODEN: ITPIDJ
INSPEC Accession Number: 6744975
Digital Object Identifier: 10.1109/34.868684
Current Version Published: 2002-08-06
Abstract
We describe a real-time computer vision and machine learning
system for modeling and recognizing human behaviors in a visual
surveillance task. The system deals in particularly with detecting when
interactions between people occur and classifying the type of
interaction. Examples of interesting interaction behaviors include
following another person, altering one's path to meet another, and so
forth. Our system combines top-down with bottom-up information in a
closed feedback loop, with both components employing a statistical
Bayesian approach. We propose and compare two different state-based
learning architectures, namely, HMMs and CHMMs for modeling behaviors
and interactions. Finally, a synthetic “Alife-style”
training system is used to develop flexible prior models for recognizing
human interactions. We demonstrate the ability to use these a priori
models to accurately classify real human behaviors and interactions with
no additional tuning or training
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