  The detection of hazardous conditions near public transit stations is
necessary for ensuring the safety and accessibility of public transit. Smart
city infrastructures aim to facilitate this task among many others through the
use of computer vision. However, most state-of-the-art computer vision models
require thousands of images in order to perform accurate detection, and there
exist few images of hazardous conditions as they are generally rare. In this
paper, we examine the detection of snow-covered sidewalks along bus routes.
Previous work has focused on detecting other vehicles in heavy snowfall or
simply detecting the presence of snow. However, our application has an added
complication of determining if the snow covers areas of importance and can
cause falls or other accidents (e.g. snow covering a sidewalk) or simply covers
some background area (e.g. snow on a neighboring field). This problem involves
localizing the positions of the areas of importance when they are not
necessarily visible.
  We introduce a method that utilizes Structure from Motion (SfM) rather than
additional annotated data to address this issue. Specifically, our method
learns the locations of sidewalks in a given scene by applying a segmentation
model and SfM to images from bus cameras during clear weather. Then, we use the
learned locations to detect if and where the sidewalks become obscured with
snow. After evaluating across various threshold parameters, we identify an
optimal range at which our method consistently classifies different categories
of sidewalk images correctly. Although we demonstrate an application for snow
coverage along bus routes, this method can extend to other hazardous conditions
as well. Code for this project is available at
https://github.com/ind1010/SfM_for_BusEdge.
