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 and Tomasi came up with something better and they called it Good Features to Track. You can read the original paper here: 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 we discussed, a good interest point detector is very useful in applications such as object tracking, object recognition, and image search.
If you apply the Shi-Tomasi corner detector to an image, you will see something like this:
As we can see here, all the important points in the frame are captured. Let's look at the code to track these features:
int main(int argc, char* argv[])
{
//...