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SMOKE+: a video dataset for automated fine-grained assessment of smoke opacity

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dc.contributor.authorSeefried, Ethan, author
dc.contributor.authorBlanchard, Nathaniel, advisor
dc.contributor.authorSreedharan, Sarath, committee member
dc.contributor.authorRoberts, Jacob, committee member
dc.date.accessioned2024-09-09T20:51:17Z
dc.date.available2024-09-09T20:51:17Z
dc.date.issued2024
dc.description.abstractComputer vision has traditionally faced difficulties when applied to amorphous objects like smoke, owing to their ever-changing shape, texture, and dependence on background conditions. While recent advancements have enabled simple tasks such as smoke detection and basic classification (black or white), quantitative opacity estimation in line with the assessments made by certified professionals remains unexplored. To address this gap, I introduce the SMOKE+ dataset, which features opacity labels verified by three certified experts. My dataset encompasses five distinct testing days, two data collection sites in different regions, and a total of 13,632 labeled clips. Leveraging this data, we develop a state-of-the-art smoke opacity estimation method that employs a small number of Residual 3D blocks for efficient opacity estimation. Additionally I explore the use of MAMBA blocks in a video based architecture, exploiting their ability to handle spatial and temporal data in a linear fashion. Techniques developed during the SMOKE+ dataset creation were then refined and applied to a new dataset titled CSU101, designed for educational use in Computer Vision. In the future I intend to expand further into synthetic data, incorporating techniques into Unreal Engine or Unity to add accurate opacity labels.
dc.format.mediumborn digital
dc.format.mediummasters theses
dc.identifierSeefried_colostate_0053N_18559.pdf
dc.identifier.urihttps://hdl.handle.net/10217/239175
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado State University. Libraries
dc.relation.ispartof2020-
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.subjectcomputer vision
dc.subjectmachine learning
dc.subjectamorphous objects
dc.subjectsynthetic data
dc.subjectdataset
dc.titleSMOKE+: a video dataset for automated fine-grained assessment of smoke opacity
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.)

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