COVARIANCE TRACKING WITH FORGETTING FACTOR AND RANDOM SAMPLING

Abstract

Covariance matching is an excellent algorithm of target tracking. In this paper, forgetting factor and random sampling methods are proposed to improve the robustness and efficiency of covariance tracking. First, a distance function between covariance matrixes is weighted by using a forgetting factor based on a fuzzy membership function to overcome the disturbances from similar targets. Then a random sampling method is applied to reduce the computing time in covariance matching and to facilitate real-time object tracking. Experiment results show that the algorithm proposed in this paper can effectively mitigate the clutter and occlusion problems at a high computing speed.