A novel fast target tracking method for UAV aerial image

Abstract

Unmanned aerial vehicles (UAV) are able to achieve autonomous flight without drivers, and UAV has been a key tool to extract space data. Therefore, how to detect the trajectories of targets from UAV aerial image sequences is of great importance. Because local features are suitable to detect target tracking, we exploit scaleinvariant feature transform (SIFT) features to describe the interesting keypoints of targets. The main innovation of this paper is to utilize Multiple hypothesis tracking (MHT) algorithm to track an object (target) in a series of image sequences. Particularly, we develop a MHT framework based on a multidimensional assignment formulation and a sliding time window policy. To obtain target tracking from UAV aerial image sequences, three steps should be done, that is, 1) Breaking each track set into tracklet at a specific time, 2) Estimating the association cost of each track set, 3) Merging trajectory fragments to a longer one iteratively. Finally, we collect several UAV aerial image sequences with different target density to construct a dataset, and experimental results demonstrate the effectiveness of the proposed algorithm.