Abstract: To address the issue of the impact of random target motion and occlusion on UAV tracking performance in complex scenarios, an autonomous UAV tracking and positioning method based on an improved CenterTrack is proposed for target monitoring. Firstly, we design a feature enhancement module to improve the tracking performance of occluded targets. Secondly, we propose a two-stage matching algorithm combined with distance-based greedy matching and stranded regions to alleviate the tracking interruption problem caused by short-time occlusion. Finally, we use a localization algorithm to assist the UAV to accurately locate the target and improve the tracking performance. We apply the proposed method to monitoring the targets in a real farm environment practically. The experimental results show that, compared to the original CenterTrack, the tracking algorithm proposed in this paper increases the multi-object tracking accuracy (M_A) by 5. 5%, the multi-object positioning precision (M_p) by 4. 3%, the identification F1 score (I_F) by 5. 5%, the number of false positives (F_P) by 779, and the number of false negatives (F_N), including missed and undetected targets, by 3 387. In addition, in real scenarios, the proposed method is able to accurately track targets that are occluded and frequently come in and out of the UAV camera's field of view. The experimental results verify that the method is feasible and effective for farm animal monitoring and tracking.