Repository logo
 

Modeling and querying uncertain data for activity recognition systems using PostgreSQL

Simple item page
dc.contributor.authorBurnett, Kevin, author
dc.contributor.authorDraper, Bruce, advisor
dc.contributor.authorRay, Indrakshi, advisor
dc.contributor.authorVijayasarathy, Leo, committee member
dc.date.accessioned2007-01-03T08:10:35Z
dc.date.available2007-01-03T08:10:35Z
dc.date.issued2012
dc.description.abstractActivity Recognition (AR) systems interpret events in video streams by identifying actions and objects and combining these descriptors into events. Relational databases can be used to model AR systems by describing the entities and relationships between entities. This thesis presents a relational data model for storing the actions and objects extracted from video streams. Since AR is a sequential labeling task, where a system labels images from video streams, errors will be produced because the interpretation process is not always temporally consistent with the world. This thesis proposes a PostgreSQL function that uses the Viterbi algorithm to temporally smooth labels over sequences of images and to identify track windows, or sequential images that share the same actions and objects. The experiment design tests the effects that the number of sequential images, label count, and data size has on execution time for identifying track windows. The results from these experiments show that label count is the dominant factor in the execution time.
dc.format.mediumborn digital
dc.format.mediummasters theses
dc.identifierBurnett_colostate_0053N_11171.pdf
dc.identifierETDF2012500148COMS
dc.identifier.urihttp://hdl.handle.net/10217/67998
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado State University. Libraries
dc.relation.ispartof2000-2019
dc.rightsCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.
dc.subjectPostgreSQL
dc.subjectViterbi
dc.titleModeling and querying uncertain data for activity recognition systems using PostgreSQL
dc.typeText
dcterms.rights.dplaThis Item is protected by copyright and/or related rights (https://rightsstatements.org/vocab/InC/1.0/). You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).
thesis.degree.disciplineComputer Science
thesis.degree.grantorColorado State University
thesis.degree.levelMasters
thesis.degree.nameMaster of Science (M.S.)

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Burnett_colostate_0053N_11171.pdf
Size:
3.03 MB
Format:
Adobe Portable Document Format
Description:
Download

Collections

2000-2019
Theses and Dissertations