Mean-Shift Tracking of Variable Kernel Based on Projective Geometry
Zhongyu Lou, Guang Jiang, Chengke Wu · 2009
The mean-shift algorithm is very useful in object tracking for its many advantages, such as good performance in real-time tracking, nonparametric density model, etc. Although the scale of the mean-shift kernel is a crucial parameter, there exists presently still no clear mechanism in choosing or updating the scale when the kernel of changing size is tracked. In this paper, a new method is introduced using projective geometry to determine the kernel size of the object. After initialization of this algorithm, we obtain the geometric information, and decide the corresponding kernel size of the object wherever the object moves. The experimental results show that this algorithm works stably and it consumes less time than traditional algorithms.