Research Mentor(s): Carol Flannagan, Research Professor
Research Mentor School/College/Department: University of Michigan Transportation Research Institute, College of Engineering
Presentation Date: Thursday, April 22, 2021
Session: Session 1 (10am-10:50am)
Breakout Room: Room 16
Computer Vision Algorithms are a fast-developing technology, and we have seen from example that they are currently not as accurate and unbiased as we hope they can be. Our project aims to develop a more efficient system and algorithm to reduce bias in computer vision programs. One way bias may be introduced into these algorithms is through issues with light levels in images and videos. Since many computer vision algorithms rely on video cameras, as opposed to infrared or another type of light, the lack of light in videos introduces uncertainty in a program, which can produce bias, where some categories of images are more accurately processed by the algorithm than others. This bias can manifest itself in different scenarios, such as during nighttime or when recording people with darker skin, and these are the biases that we aim to correct. My part in the project involved labelling the videos that are going to be used for analysis for the algorithms, and attempting to help create a standardized method of labelling in order to have a set of videos with which the algorithm can be trained with. Our sample set was purposefully selected to have a variety of videos with different light levels and skin tones. Our ultimate purpose was to label as many videos as possible to use later on in the project, where other groups are working on developing the algorithm and all other overarching parts of the project. The main project was not completed, and likely will not for some years, but we achieved our loose goal of labelling videos.