Contour tracking using Gaussian particle filter

Gaussian particle filter algorithm provides a framework to estimate the state of a moving object. However, it is a known fact that parameters like noise variance and particle number affect the effectiveness of the filter greatly. To improve the performance of Gaussian particle filter in contour tracking, the authors propose a parameter adapting mechanism. To simplify the filter´s implementation, a variant sampling method is also proposed. This sampling method combines sampling step with prediction step by taking advantage of the Gaussian assumption and by exploring the linear structure of the system dynamic model. Finally, comparative experiments are provided, which demonstrate the merits of the proposed algorithm.