An improved online multiple object tracking algorithm based on KFHT motion compensation model in the aerial videos

Due to the rotation of unmanned aerial vehicle, the position of object in the image could shift a lot which easily leads to tracking failure. To solve this problem, a motion compensation model based on Kalman Filter and Homography Transformation (KFHT) is designed in this paper to predict the position of trackers and to compensate position offset. And then an improved online multiple object tracking algorithm based on KFHT is proposed. In our algorithm, object appearance feature is extracted by residual CNN, the feature similarity and location association of objects are utilized to accomplish the object discrimination by two stage matching. To verify the effectives of the improved algorithm, experimental evaluation is carried out on the VisDrone2019 dataset by using YOLOv5 detection results and prior ground truth respectively. Results demonstrate that the algorithm given in this paper reduces the number of identity switches by 17% with YOLOv5 and by 66% with prior ground truth, and increases the tracking accuracy about 1.5% and 3.6% in MOTA respectively. The experimental results show that our algorithm based on the KFHT model is effective.