Particle Filter Tracking Algorithm based on Dynamic Feature Fusion

Abstract

A particle filter object tracking algorithm based on dynamic feature fusion is proposed in this paper.The presented algorithm uses the complementary features, which are gray histogram and gradient histogram, to represent the object model.In the tracking procession, the confidence for each feature is adjusted according to the discrimination between the object and the background, and the object model is dynamic online established and updated.The presented method can improve the accuracy of the object modeling and furthermore improve the accuracy of the particle filter tracking algorithm.Experimental results have demonstrated the effectiveness of our approach.