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Learning people movement model from multiple cameras for behaviour recognition

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posted on 2004-01-01, 00:00 authored by N Nguyen, Svetha VenkateshSvetha Venkatesh, G West, H Bui
In surveillance systems for monitoring people behaviours, it is important to build systems that can adapt to the signatures of people's tasks and movements in the environment. At the same time, it is important to cope with noisy observations produced by a set of cameras with possibly different characteristics. In previous work, we have implemented a distributed surveillance system designed for complex indoor environments [1]. The system uses the Abstract Hidden Markov mEmory Model (AHMEM) for modelling and specifying complex human behaviours that can take place in the environment. Given a sequence of observations from a set of cameras, the system employs approximate probabilistic inference to compute the likelihood of different possible behaviours in real-time. This paper describes the techniques that can be used to learn the different camera noise models and the human movement models to be used in this system. The system is able to monitor and classify people behaviours as data is being gathered, and we provide classification results showing the system is able to identify behaviours of people from their movement signatures.

History

Title of book

Structural, syntactic, and statistical pattern recognition : joint IAPR international workshops SSPR 2004 and SPR 2004, Lisbon, Portugal, August 18-20, 2004 : proceedings

Series

Lecture notes in computer science ; 3138

Chapter number

33

Pagination

315 - 324

Publisher

Springer-Verlag

Place of publication

Berlin, Germany

ISSN

0302-9743

ISBN-13

9783540225706

ISBN-10

3540225706

Language

eng

Publication classification

B1.1 Book chapter

Copyright notice

2004, Springer-Verlag Berlin Heidelberg

Extent

127

Editor/Contributor(s)

A Fred, T Caelli, R Duin, A Campilho, D de Ridder

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