Surface Multiple Object Tracking: An Accurate HAT-YOLOv8-ADT Tracking Model

Abstract

With the development of artificial intelligence technology, Autonomous aerial vehicles (AAV) have the ability to sense the environment. multiple object tracking (MOT) in AAV video is a very important vision task with a wide variety of applications. However, there are still many challenges in MOT in AAV video. First, the movement of the onboard camera in the three-dimensional (3-D) direction during the tracking process, as well as the unpredictable measurement noise characteristics of AAVs flying at high speeds, can lead to significant deviations in the prediction of the object’s position. Second, the applicability of the traditional detection algorithm decreases when the object is small and dense in the AAV viewpoint during detection. Finally, the traditional intersection over union (IoU) matching approach does not take into account the effects of the height and width of the box, and the matching results are inaccurate for the prediction and detection box. In order to address these challenges, we recommend the adaptive DeepSort (ADT) algorithm to reduce the prediction bias due to camera movement and difficulty in predetermining measurement noise characteristics, the hybrid attention transformer-YOLOv8 (HAT-YOLOv8) algorithm to enhance the detection capability of tiny objects, and the IoU of height and width (HWIoU) matching algorithm, which improves the matching accuracy and thus the tracking accuracy. Experimental results show that our proposed solution outperforms the baseline solution. It outperforms the current mainstream StrongSort in MOTA, HOTA and IDF1 by 2.86%, 0.9%, and 9.36%. Code repository link: https://github.com/networkcommunication/.