Senior Design Expo 2019

A Baseball Strike Zone Delineator
Implemented Using Digital Image
Processing Techniques
Joon Mo Park
Advisor: David Vallancourt
Contrary to the popularly held notion
that the baseball strike zone is rectangular
and is fixed, the strike zone is actually
constantly changing as the batter is making
movements to hit the baseball and has a
shape of a pentagonal prism. The proposed
delineator calculates for the position of the
strike zone using input images obtained
from the two set-up cameras and using
digital image processing techniques. The
delineator aims to aid the umpires in the
process of determining whether a pitch is a
strike or a ball by providing them an
environment where they can compare their
calls with those calculated from the
algorithm. It should be noticed the main
purpose of the proposed delineator is for the
training of the umpires. Therefore, the batter
wears red LEDs, that are used to track the
batter’s shoulders, top of the uniform pants,
and the kneecap with the help of digital
image processing techniques.
There exist three main objectives for
the implementation of the strike zone
delineator: the detection of the baseball, the
home plate, and the batter’s body. The
detection of the baseball is achieved using
the idea that the baseball is circular and
white. Therefore, an algorithm that looks for
round objects in an image was developed in
order to successfully detect the baseball.
After the conversion of rgb input images to
grayscale images, binarization was
implemented, and the metric that indicates
the roundness of objects was calculated. The
binarization is essential in the
implementation of the delineator since it
enables the detection of the home plate. The
binarization of images detects boundaries of
different objects in an image, and it is for
certain that one of the objects detected is the
home plate of interest. The only problem
now is to figure out which detected object is
the home plate. The Speeded-Up Robust
Features (SURF) algorithm was
implemented in the process of filtering out
detected objects that are not the home plate.
Matlab’s object recognition using SURF is
composed of three steps: feature extraction,
feature description, and feature matching. In
other words, the SURF algorithm enables
the detection of an object that is similar to
an inputted reference image, which in this
case would be the home plate. The last
objective is the detection of the batter’s
body. More specifically, the batter’s
shoulders, top of the uniform pants, and the
kneecap must be all detected in order to
calculate for the strike zone. The batter
would wear red LEDs for the
implementation of the red segment filtering
algorithm. The idea of the red segment
filtering algorithm is that all pixels of the
rgb images have values for their red, green,
and blue components. The algorithm
subtracts the grayscale values of images
from those containing only the images’ red
components.
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