Title:
Human Detection for Flood Rescue: Application of YOLOv5 Algorithm and DeepSort Object Tracking

dc.contributor.advisor Taylor, John E.
dc.contributor.author Lewert, Jessica
dc.contributor.committeeMember Amekudzi-Kennedy, Adjo
dc.contributor.committeeMember Marks, Eric
dc.contributor.committeeMember Toelle, James
dc.contributor.department Civil and Environmental Engineering
dc.date.accessioned 2022-01-14T16:08:14Z
dc.date.available 2022-01-14T16:08:14Z
dc.date.created 2021-12
dc.date.issued 2021-12-15
dc.date.submitted December 2021
dc.date.updated 2022-01-14T16:08:14Z
dc.description.abstract This thesis proposes a method of human detection using high-resolution surveillance cameras to monitor sections of the Chattahoochee River that require frequent search and rescue efforts due to flooding. The areas of interest are located in the city of Columbus, Georgia. The goals of this study are to evaluate the effectiveness of the YOLO (You Only Look Once) algorithm for human detection on the river as well as to propose future improvements to the city’s existing alert methods in the event of a flood.
dc.description.degree M.S.
dc.format.mimetype application/pdf
dc.identifier.uri http://hdl.handle.net/1853/66083
dc.publisher Georgia Institute of Technology
dc.subject Flood
dc.subject Object detection
dc.subject YOLO
dc.subject DeepSort
dc.subject Chattahoochee River
dc.title Human Detection for Flood Rescue: Application of YOLOv5 Algorithm and DeepSort Object Tracking
dc.type Text
dc.type.genre Thesis
dspace.entity.type Publication
local.contributor.advisor Taylor, John E.
local.contributor.corporatename School of Civil and Environmental Engineering
local.contributor.corporatename College of Engineering
relation.isAdvisorOfPublication 24e00c16-070d-48b1-b9aa-549e633ae5b6
relation.isOrgUnitOfPublication 88639fad-d3ae-4867-9e7a-7c9e6d2ecc7c
relation.isOrgUnitOfPublication 7c022d60-21d5-497c-b552-95e489a06569
thesis.degree.level Masters
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