Classifying and detecting activity based patterns using relational context and probabalistic models in video
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Authors
Swears, Eran
Issue Date
2015-05
Type
Electronic thesis
Thesis
Thesis
Language
ENG
Keywords
Computer Systems engineering
Alternative Title
Abstract
Many scenes in sports, surveillance, and other video domains involve both simple activities and complex multi-agent activities where the moving agents (e.g. pedestrians, vehicle) interact with their surroundings and each other. This dissertation examines how to characterize these interactions by incorporating relational context into models that will enable the automatic recognition of scene elements (e.g. buildings, roadways) and complex activities (e.g. Person-Unload-Vehicle) to reduce the burden on video analysts. Relational context can be anything from known relationships between moving and stationary objects such as a vehicles driving on roadways, to the more explicit self constraints (e.g. "Probability(vehicle)") or pairwise semantic constraints (e.g. "Near","Before", "same-direction", "Track-IDs-Different") between two moving objects (e.g. person, vehicle).
Description
May 2015
School of Engineering
School of Engineering
Full Citation
Publisher
Rensselaer Polytechnic Institute, Troy, NY