Classifying and detecting activity based patterns using relational context and probabalistic models in video
Authors
Swears, Eran
ORCID
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Other Contributors
Boyer, Kim L.
Radke, Richard J., 1974-
Stewart, Charles V.
Sanderson, A. C. (Arthur C.)
Hoogs, Anthony J.
Radke, Richard J., 1974-
Stewart, Charles V.
Sanderson, A. C. (Arthur C.)
Hoogs, Anthony J.
Issue Date
2015-05
Keywords
Computer Systems engineering
Degree
PhD
Terms of Use
This electronic version is a licensed copy owned by Rensselaer Polytechnic Institute, Troy, NY. Copyright of original work retained by author.
Full Citation
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
Department
Dept. of Electrical, Computer, and Systems Engineering
Publisher
Rensselaer Polytechnic Institute, Troy, NY
Relationships
Rensselaer Theses and Dissertations Online Collection
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