Multi-scale object tracking based on mean shift and AUC

The mean-shift algorithm is an efficient technique for 2D object tracking. Although the scale of the mean-shift kernel is a crucial parameter, there is presently no clean mechanism for choosing or updating scale while tracking objects that are changing in size. In this paper, Lindeberg´s theory of scale selection based on local maxima of differential scale-space filters is improved and then adapted to select suitable scale of the mean-shift kernel in the process of multi-scale object tracking. In addition, AUC is regarded as a standard to evaluate the efficiency of the algorithm put forward above.