First Advisor

Wu-chi Feng

Term of Graduation

Fall 2023

Date of Publication


Document Type


Degree Name

Doctor of Philosophy (Ph.D.) in Computer Science


Computer Science




Nighttime Pedestrian Detection, Object Detection



Physical Description

1 online resource (xiv, 98 pages)


"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.


© 2023 Afnan Althoupety

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Available for download on Saturday, December 07, 2024