[Picture of me]

Emily Yu


Williams College Class of 2011
Email Me

Home
[Williams Eph]


The first step in implementing a template matching version of the blink detection was to find templates. Using the method to detect blinks from the previous page, I was able to save templates of the open and closed eyes. In the interface, the user blinked repeatedly in the search boxes like so:

[blink detector version 3]

When a good blink template was found, the images were saved like this:

[eye templates]

Then, using a function from the OpenCV library called CVmatchTemplate, I tried to find a match in the video frames. However, when using this, I found that I could rarely correctly find a match using the function. Professor Betke suggested that by narrowing the search area, I might be able to find a match better, so I revised my code to find a search area based on the trackpoint (the coordinate of the feature being tracked). This is how it works:

[search area]

I found the distance from the trackpoint to the edges of the template boxes. I created a rectangle and shifted it as the trackpoint moved. When this still didn't yielf accurate results using matchTemplate, I decided to write my own method to find the Normalized Correlation Coefficient.

Forward
Back