Variable module graphs: A framework for inference and learning in modular vision systems

Amit Sethi, Mandar Rahurkar, Thomas S. Huang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We present a novel and intuitive framework for building modular vision systems for complex tasks such as surveillance applications. Inspired by graphical models, especially factor graphs, the framework allows capturing the dependencies between different variables in form of a graph. This enforces principled coordination and exchange of information between different modules. Breaking away from the traditional probabilistic graphical models the framework allows flexibility of design in individual modules by allowing different learning and inference mechanisms to work in a common setting. It also allows easy integration of more modules into an already functional system. We demonstrate the ease of building a complex vision system within this framework by designing a fully automatic multi-target tracking system for a video surveillance scenario. Favorable results are obtained for the tracking application.

Original languageEnglish (US)
Title of host publicationIEEE International Conference on Image Processing 2005, ICIP 2005
PublisherIEEE Computer Society
Pages1323-1326
Number of pages4
ISBN (Print)0780391349, 9780780391345
DOIs
StatePublished - 2005
EventIEEE International Conference on Image Processing 2005, ICIP 2005 - Genova, Italy
Duration: Sep 11 2005Sep 14 2005

Publication series

NameProceedings - International Conference on Image Processing, ICIP
Volume2
ISSN (Print)1522-4880

Other

OtherIEEE International Conference on Image Processing 2005, ICIP 2005
Country/TerritoryItaly
CityGenova
Period9/11/059/14/05

ASJC Scopus subject areas

  • Engineering(all)

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