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

With the development of artificial intelligence tech-
nology, 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 (3D) 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 appli-
cability 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 intersection over union of height and width
(HWIoU) matching algorithm, which improves the matching ac-
curacy 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/.