Railroad practice and FRA regulations require regular, visual inspection of track in
order to monitor its condition and detect potential defects.
Machine vision has proven
valuable in a variety of railroad inspection applications due to its
ability to quickly,
accurately and objectively record and process large amounts of video and image data.
Over the past decade, machine vision has begun to be applied to inspection of railroad
track components with inspection of certain parts of the track
However, other aspects are more challenging and are still in various
stages of research and development.
Among the latter is the condition and position of
cut spikes and rail anchors.
To detect these components it is useful t
o identify larger
components such as ties and tie plates and use these as reference points to identify
other components and characteristics.
Many of these components occur periodically
processing techniques can be used to detect and segment the
accomplish this, an image or video is converted into one
dimensional signals and then
spectral estimation is applied to those signals in order to detect periodicity.
periodically repeating objects are then detected and segmented.
will present research on development of an unsupervised method for
detecting periodically occurring components that are repeating in one direction.
railroad track inspection, detecting and extracting the periodic components that appear
in inspection im
ages is useful because these components are often indicative of
overall track condition.
The method will be demonstrated on track inspection
panoramas and on turnout inspection videos.
Then, a more general method will be
described that detects and locali
zes periodically occurring objects in images that
repeat in arbitrary directions.
In this method, it is assumed that an object repeats
along one unknown direction within the two
dimensional image. The goal is to detect
that direction of periodicity, and t
o localize those periodic objects within the image.
This method will be illustrated on example images, including track inspection
Esther Resendiz is a Ph.D. candidate in the Department of Electrical and Computer
Engineering at the U
niversity of Illinois at Urbana
She is a member of
the Computer Vision and Robotics Laboratory (CVRL) in Beckman Institute.
primarily focuses on signal processing
based methods for image and video analysis.
She collaborates frequently wit
h the Railroad Engineering Program in the Civil &
Environmental Engineering Department, and is currently applying machine vision to
Her previous work includes undercarriage inspection for passenger
She is also the President a
founder of Fashion Latte Inc.,
a visual search startup for the online apparel domain that
she founded in 2008.
Prior to joining the CVRL, she received an M.S. in Electrical
and Computer Engineering from UIUC and a B.S. i
n Electrical Engineering from the
University of Texas at Austin.