A Comparison of Different Approaches to Nonlinear Shift Estimation for Object Tracking

This paper presents a corner based voting method for estimating the object shift in video image frames. Information about the corners distribution around a reference point is used to represent the object shape and then to find the most probable target position in the next frame. Tracking is done through using a voting space obtained by matching corners information. A motion vector for the reference point is nonlinearly estimated with three different strategies by using the global information of the matched corners. The results show a comparison between three considered strategies for estimating the object shift.