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

Swarup Medasani; Yuri Owechko

doi:10.1117/12.747437

26 September 2007 Evolutionary optimization and graphical models for robust recognition of behaviors in video imagery

Swarup Medasani, Yuri Owechko

Proceedings Volume 6712, Unconventional Imaging III; 67120J (2007) https://doi.org/10.1117/12.747437
Event: Optical Engineering + Applications, 2007, San Diego, California, United States

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.

Citation Download Citation

Swarup Medasani and Yuri Owechko "Evolutionary optimization and graphical models for robust recognition of behaviors in video imagery", Proc. SPIE 6712, Unconventional Imaging III, 67120J (26 September 2007); https://doi.org/10.1117/12.747437

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