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Proceedings Paper

Evolutionary optimization and graphical models for robust recognition of behaviors in video imagery
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Paper Abstract

Behavior analysis deals with understanding and parsing a video sequence to generate a high-level description of object actions and inter-object interactions. We describe a behavior recognition system that can model and detect spatio-temporal interactions between detected entities in a visual scene by using ideas from swarm optimization, fuzzy graphs, and object recognition. Two extensions of the Particle Swarm Optimization algorithm are explored, one uses classifier based object recognition to first detect entities in video scenes and then employs fuzzy graphs to model the associations while the second extension directly searches for graph based object associations. Our hierarchical generic event detection scheme uses fuzzy graphical models for representing the spatial associations as well as the temporal dynamics of the discovered scene entities. The spatial and temporal attributes of associated objects and groups of objects are handled in separate layers in the hierarchy. We also describe a new behavior specification language that helps the user easily describe the event that needs to be detected using simple linguistic or graphical queries. Preliminary results are promising and studies are underway to evaluate the use of the system in more complicated scenarios.

Paper Details

Date Published: 26 September 2007
PDF: 12 pages
Proc. SPIE 6712, Unconventional Imaging III, 67120J (26 September 2007); doi: 10.1117/12.747437
Show Author Affiliations
Swarup Medasani, HRL Labs., LLC (United States)
Yuri Owechko, HRL Labs., LLC (United States)


Published in SPIE Proceedings Vol. 6712:
Unconventional Imaging III
Jean J. Dolne; Victor L. Gamiz; Paul S. Idell, Editor(s)

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