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dc.contributor.advisorMavris, Dimitri N.
dc.contributor.authorCommun, Domitille Marie, France
dc.date.accessioned2019-05-29T14:04:53Z
dc.date.available2019-05-29T14:04:53Z
dc.date.created2019-05
dc.date.issued2019-04-26
dc.date.submittedMay 2019
dc.identifier.urihttp://hdl.handle.net/1853/61313
dc.description.abstractIn the United States alone, 5,987 pedestrians were killed and 70,000 injured in 2016 and 2015 respectively. Those numbers are of particular concern to universities where traffic accidents and incidents represent one of the main causes of injuries on campuses. On the Georgia Tech Campus, the growth of the population-to-infrastructure ratio, the emergence of new transportation systems, and the increase in the number of distractions have shown to have an impact on pedestrian safety. One means to ensure safety and fast responses to incidents on campus is through video surveillance. However, identifying risky situations for pedestrians from video cameras and feeds require significant human efforts. Computer vision and other image processing methods applied to videos may provide the means to reduce the cost and human errors associated with processing images. Computer vision in particular provides techniques that enable artificial systems to obtain information from images. While many vendors provide computer vision and image recognition capabilities, additional efforts and tools are needed to support 1) the mission of the Georgia Tech Police Department and 2) the identification of solutions or practices that would lead to improved pedestrian safety on campus. Data from cameras can be systematically and automatically analyzed to provide improved situational awareness and help to automate and better inform enforcement operations, identify conflict situations including pedestrians and provide calibration data to optimize traffic light control. In particular, this thesis aims at developing an intelligent system that automates data collection about incidents around campus and attempts to optimize traffic light control. This is achieved by: 1) Leveraging computer vision techniques such as object detection algorithms to identify and characterize conflict situations including pedestrians. Computer vision techniques were implemented to detect and track pedestrians and vehicles on surveillance videos. Once trajectories were extracted from videos, additional data such as speed, collisions and vehicle and pedestrian flows were determined. Such data can be used by the Georgia Tech Police Department to determine needs for agents to manage traffic at a given intersection. Speed information is used to detect speeding automatically, which can help to enforce law in an automated way. Traffic and walking light color detection algorithms were implemented and combined with location data to detect jaywalking and red light running. The conflict situations detected were stored in a database which completes the Police record database. The data is structured such as to enable statistics or the detection of patterns with improved processing time. Hence, the tool built in this thesis provides structured information about violations and dangerous situations around campus. This data can be used by the Police Department to automate law enforcement and issue citations automatically and to determine the needs for countermeasures to ensure pedestrian safety. 2) Implementing a simple optimized traffic light control system and setting up the inputs necessary for a an improved optimization of traffic light control using reinforcement learning. It is expected that the improved situational awareness and information gained from developing these capabilities will contribute to help reduce the number of collisions, the amount of dangerous jaywalking, and lead to new ways to ensure pedestrian safety on campus
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.publisherGeorgia Institute of Technology
dc.subjectpedestrian safety, computer vision, surveillance cameras, campus intersections
dc.titleEnsuring pedestrian safety on campus through the use of computer vision
dc.typeThesis
dc.description.degreeM.S.
dc.contributor.departmentAerospace Engineering
thesis.degree.levelMasters
dc.contributor.committeeMemberHunnicutt, Jeffrey Mr
dc.contributor.committeeMemberPinon Fischer, Olivia Dr
dc.contributor.committeeMemberBalchanos, Michael Dr
dc.date.updated2019-05-29T14:04:53Z


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