Shi-Tomasi Corner Detection is an algorithm used in computer vision to identify corners in images. It is based on the idea that corners are points where there is a significant change in intensity in multiple directions. The method evaluates the local structure of the image using a matrix called the autocorrelation matrix, which helps determine the strength of corners. This technique is an improvement over the earlier Harris Corner Detection method, providing better performance in certain scenarios. It is widely used in applications like object tracking, image stitching, and feature matching, making it a fundamental tool in the field of image processing.