Obstacle Detection On A Track From A Quiescent
Image Of A Tram’s Front View
Author(s)
T. Katori & T. Izumi
Abstract
Tram systems are being re-evaluated worldwide, in order to provide a barrierfree,
low-building cost transport system. New systems are opening and the
existing lines expanding. However, there are many anxieties about the
occurrence of traffic accidents with tram vehicles. The safety of the tram vehicle
is still solely dependent upon the observation of the driver. However, ITS
technology for road transport has advanced enough to assist with this issue.
Therefore, we construct a safe support system for tram drivers, to detect
obstacles using front view images of trams by image processing. In this paper,
we propose a method to detect obstacles on or around tracks from the quiescent
images.
There are private or common tracks for tram systems; the more dangerous
objects are on the common tracks, because the tram vehicle runs on its own track
only and cannot avoid the obstacles.
A tram vehicle can only run on tracks. This fact means that an exact path in
front of the tram is known. So once the track is detected, the safety of trams is
maintained by observation around the tracks. On the images, head on tracks are
vivid and the rut is dark, the tracks are detected by edge extraction in image
processing. If an object’s shadow exists on the tracks, the object is detected as an
obstacle. If an obstacle has height, the object projects a wide area on a bird’s
view map, but in the case where tracks have no height, they have a small area on
the map; the obstacles are judged by these characteristics.
This method can detect both moving and quiescent obstacles, because this
process uses a quiescent image. We show some good results by the presented
process for a 616 real tram’s quiescent front view images.
Keywords: tram vehicle, safety driving support, obstacle detection, quiescent
image, track extraction.
Keywords
tram vehicle, safety driving support, obstacle detection, quiescent
image, track extraction
Related Book
Other papers in this volume
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