Capturing videos of trains under ambient lighting conditions for computer vision analysis
Gopalakrishnan, Suchithra
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https://hdl.handle.net/2142/42223
Description
Title
Capturing videos of trains under ambient lighting conditions for computer vision analysis
Author(s)
Gopalakrishnan, Suchithra
Issue Date
2013-02-03T19:28:23Z
Director of Research (if dissertation) or Advisor (if thesis)
Ahuja, Narendra
Department of Study
Electrical & Computer Eng
Discipline
Electrical & Computer Engr
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
Machine Vision system
railroad monitoring
Abstract
Intermodal trains are usually the fastest freight trains in North America. Fuel con-
sumption in these trains is high because of their aerodynamic characteristics. If the
loads on these railcars were placed in better con gurations, it would be possible to
reduce fuel consumption. In order to inspect their trains for their aerodynamic char-
acteristics, Burlington Northern Santa Fe (BNSF) has sponsored a project to build a
wayside machine vision (MV) system in Sibley, MO, a busy intermodal freight train
route called TRANSCON. Our research group at the Beckman Institute, in collab-
oration with the Civil Engineering Railroad program, has developed a wayside MV
system that captures videos of trains passing by. It consists of various train detection
sensors, personal computers, and camera and lighting towers. After the video data is
collected, it is analyzed using the Train Monitoring System (TMS) and Train Scor-
ing System (TSS) algorithms. Finally, each car in the train is given an aerodynamic
score. Currently, the system is functional in analyzing videos that have been acquired
in daylight.
This work analyzes the requirements of the TMS algorithm, improves TMS per-
formance on daytime trains, and reports the design of a lighting system that will be
used to provide su cient lighting for the scene at night or when there is not enough
daylight. This work investigates the causes of the problems with exposure, and uses
non-realtime adjustment of the camera to acquire properly exposed videos. The re-
sults show improvement in TMS results. Also discussed is the groundwork done for
the lighting sub-system.
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