Recursive Image Segmentation for Vehicular Traffic Analysis
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Date
2020Author
Eeshwara, Manusha
Thilakumara, Rohana
Amarasingha, Niranga
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Show full item recordAbstract
Many methods have been
proposed for image segmentation in
vehicular traffic analysis using traffic camera
video footage. However, isolation of moving
objects with perfect object boundaries has
been a challenging problem in vehicular
traffic analysis. Usually these vehicle objects
are extracted inside rectangular boundaries
with extra irrelevant background image
pixels from other objects included in the
analyzed image. Thus using such
segmentation methods in vehicle
identification using video is not favorable for
feature extraction for classification of vehicle
category. This work proposes a method to
deal with irregular shaped image
segmentation for vehicle identification using
a recursive algorithm. A binary thresholded
image composed of white and black pixels is
filtered with a 2D low pass filter to isolate
irregular shaped image boundaries of
objects. Then recursive image segmentation
is applied on the filtered binary image. White
pixels in the 2D filtered image are used to
identify the presence of the object. If the
neighboring pixels of the pixel of interest are
also white, then those neighboring pixels are
recursively processed the same way to
account for the extent of the object. This
recursive collection of pixels bounded by an
irregular shaped boundary is continued until
neighboring pixels are significantly different
in color from the pixel of interest. From this
recursive image segmentation algorithm,
extraction of all pixels of odd shaped objects
done in an efficient manner. Accordingly,
pixels count, height and the width of the
object are recorded. This image
segmentation method has been successfully
applied to identify vehicle categories in
traffic video sequences.
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