Sponsor
Portland State University. Department of Computer Science
First Advisor
Wu-chi Feng
Term of Graduation
Fall 2023
Date of Publication
12-7-2023
Document Type
Dissertation
Degree Name
Doctor of Philosophy (Ph.D.) in Computer Science
Department
Computer Science
Language
English
Subjects
Nighttime Pedestrian Detection, Object Detection
DOI
10.15760/etd.3701
Physical Description
1 online resource (xiv, 98 pages)
Abstract
"At some point in the day, everyone is a pedestrian" a message from the National Highway Traffic Safety Administration (NHTSA) about pedestrian safety. In 2020, NHTSA reported that 6,516 pedestrians were killed in traffic crashes and a pedestrian was killed every 81 minutes on average in the United States. In relation to light condition, 77% of pedestrian fatalities occurred in the dark, 20% in daylight, 2% in dusk, and 2% in dawn.
To tackle the issue from a technological perspective, this dissertation addresses the problem of pedestrian detection robustness in dark conditions, benefiting from image processing and learning-based approaches by: (i) proposing a pedestrian-luminance-aware brightening framework that moderately corrects image luminance so that pedestrians can be more robustly detected, (ii) proposing an image-to-image translation framework that learns the mapping between night and day domains through the game training of generators and discriminators and thus alleviates detecting dark pedestrian using the synthetic night images, and (iii) proposing a multi-modal framework that pairs RGB and infrared images to reduce the light factor and make pedestrian detection a fair game regardless the illumination variance.
Rights
© 2023 Afnan Althoupety
In Copyright. URI: http://rightsstatements.org/vocab/InC/1.0/ This Item is protected by copyright and/or related rights. 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).
Persistent Identifier
https://archives.pdx.edu/ds/psu/41138
Recommended Citation
Althoupety, Afnan, "Seeing in the Dark: Towards Robust Pedestrian Detection at Nighttime" (2023). Dissertations and Theses. Paper 6569.
https://doi.org/10.15760/etd.3701