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Proceedings Paper

Behavior recognition using cognitive swarms and fuzzy graphs
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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. In this paper, 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. Extensions of Particle Swarm Optimization based approaches for object recognition are first used to detect entities in video scenes. 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 analyst easily describe the event that needs to be detected using either simple linguistic queries or graphical queries. Our experimental results show that the approach is promising for detecting complex behaviors.

Paper Details

Date Published: 30 April 2007
PDF: 9 pages
Proc. SPIE 6560, Intelligent Computing: Theory and Applications V, 656005 (30 April 2007); doi: 10.1117/12.720089
Show Author Affiliations
Swarup Medasani, HRL Labs., LLC (United States)
Yuri Owechko, HRL Labs., LLC (United States)

Published in SPIE Proceedings Vol. 6560:
Intelligent Computing: Theory and Applications V
Kevin L. Priddy; Emre Ertin, Editor(s)

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