Object tracking from airborne video using particle filters algorithm on dynamic feature fusion

Abstract

A particle filter object video tracking algorithm based on dynamic feature fusion is proposed in this paper. This algorithm uses the complementary features, which are gray histogram and gradient histogram, to represent the object model. In the process of tracking, the confidence for each feature is adjusted according to the discrimination between the object and the background, and the object model is dynamically established and updated online. 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 for airborne platform video.