The Shi-Tomasi Corner Detector is an algorithm used in computer vision to identify corner points in images. It is based on the idea that corners are areas where there is a significant change in intensity in multiple directions. This method improves upon the Harris Corner Detector by providing a more reliable measure of corner strength, making it effective for various applications like object tracking and image matching. The algorithm calculates the eigenvalues of the gradient covariance matrix to determine corner quality. If the minimum eigenvalue exceeds a certain threshold, the point is classified as a corner. This technique is widely used in conjunction with other methods, such as Feature Matching and Optical Flow.