The Harris corner detector performs well in many cases, but it can still be improved. Around six years after the original paper by Harris and Stephens, Shi-Tomasi came up with something better and they called it Good Features To Track. You can read the original paper at: http://www.ai.mit.edu/courses/6.891/handouts/shi94good.pdf. They used a different scoring function to improve the overall quality. Using this method, we can find the N strongest corners in the given image. This is very useful when we don't want to use every single corner to extract information from the image. As discussed earlier, a good interest point detector is very useful in applications, such as object tracking, object recognition, image search, and so on.
If you apply the Shi-Tomasi corner detector to an image, you will see something like this:
As you can see here, all the important points in the frame are captured. Let's take a look at the following code to track these features:
int main(int...