# Ball Tracking Based on Multiscale Feature Enhancement and Cooperative Trajectory Matching
###### Abstract
For instance, [PERSON] et al. [16], [PERSON] et al. [17], and [PERSON] [15] all employ the YOLO series of methods, while [PERSON] et al. [18] employ several classic general lightweight detection models. While this straightforward adaptation may be convenient, it often overlooks the unique challenges encountered in sports scenarios, such as motion blur, occlusion, and other issues. Although specific progress has been made in ball tracking in sports videos, these methods use the same method to uniformly handle balls and human objects on the court in sports analysis systems, ignoring the analysis of the unique attributes of ball objects. First, the number of active ball objects on the playing field is far smaller than that of humans. There is only a fixed number of balls moving on the court, unlike the number of players, which is uncertain. The trajectory of the ball is fixed and continuous to the same target, while the trajectories of the players on the court belong to multiple players with different identities. Second, ball objects are smaller in size. The ball object covers a small pixel area, resulting in relatively few available features, which could be more conducive to extracting abstract-level features. Third, locating the bounding box of the ball is more challenging. Under the IoU evaluation standard, balls occupy a small proportion of pixels in the image, causing the error impact caused by the offset of the object bounding box to be much larger than the general scale. Fourth, due to the complexity of the background and movement in sports competition videos, ball objects can easily blend into the background and become difficult to detect. Due to its high speed of movement, the ball usually appears blurred in the image. Precise boundary determination is difficult, which brings greater challenges to detection and tracking. Based on the above analysis and benefiting from small object detection and multiobject tracking, this paper proposes a ball detection and tracking method based on multiscale feature enhancement and multilevel collaborative matching to improve tracking results from multiple angles. Specifically, for the complex problem of the ball being too small and moving on the court, our goal is to capture more discriminating features and explore tracking algorithms based on the ball's motion characteristics. Through data enhancement, the size of the data set is expanded, the diversity of the data set is enriched, and the proportion of small ball object samples is increased to enhance the generalization ability of the detection model. Multiscale feature learning can integrate the spatial location information of low-level features and the abstract semantic information of high-level features to improve small object detection capabilities while reducing the amount of calculation as much as possible. A certain correlation exists between objects and objects and between objects and backgrounds. By capturing this potential relationship context, detection performance can be improved. Considering object recall, positive and negative sample distribution, and calculation amount, the prior anchor frame is abandoned, and the confidence of the object's existence and the bounding box coordinates is directly predicted through key point estimation, which improves the performance of small object detection while reducing problems. Complexity. The ball constantly tracks the same object, avoiding the ID assignment process during the tracking process. Unlike single-object tracking, all links in the object motion analysis process of sports competition videos are automatic, and there is no prior information about the object position and category in the first frame. Therefore, we use a simplified multiobject tracking method and rule matching to generate the ball's trajectory online. Then, based on the characteristics of the generated ball trajectory, we automatically correct it through a simple linear motion model to improve the accuracy of tracking in batch processing.