Meanshift Tracking Presentation

The document describes the mean shift algorithm and its application to object tracking in computer vision. Mean shift is an iterative procedure that moves data points to the average of nearby points, converging at modes of the data's probability density function. It can be used for tracking by modeling a target object's color distribution and applying mean shift to match candidate locations in subsequent frames. The algorithm maximizes the Bhattacharyya coefficient between color distributions to find the best match for the target's new location in each frame.