Tracking by Classification

Mason Matthews, Carlo Tomasi

When tracking objects in video, photometric and geometric changes can decrease an algorithmís ability to follow the target. Motion models are often used to lessen this effect and improve tracker accuracy, but as with all models, incorrect assumptions can lead to very poor performance. In order to avoid this potential degradation, this project seeks to understand how effectively an algorithm can track without a motion model.