# Ball Tracking Based on Multiscale Feature Enhancement and Cooperative Trajectory Matching
## 4 Experiments
### Performance Comparison with Other Ball-Tracking Methods  
In order to fully evaluate the effectiveness and advancement of our proposed method, we selected six of the currently best-performing single-object tracking methods and open-source ball-tracking methods in the comparative experimental evaluation of ball-tracking results, including UNINEXT [41], OSTrack [42], STACK [43], Deepsport [44], VolleyVision [45], and EKF [8]. UNINEXT reformulates diverse instance perception tasks into a unified object discovery and retrieval paradigm and can flexibly perceive different types of objects by simply changing the input prompts. OSTrack unifies feature learning and relationship modeling by connecting template search image pairs with bidirectional information flow. Through mutual guidance, discriminative object-oriented features can be dynamically extracted. In the in-network candidate early elimination module, a strong similarity prior to calculation in a single-stream framework is added, which can significantly improve the reasoning efficiency. STACK transforms object tracking into a direct bounding box prediction problem and predicts it through an encoder-decoder transformer structure. The encoder models the global spatiotemporal feature dependencies between the object object and the search region, while the decoder learns a query embedding to predict the spatial location of the object object. Deepsport proposes to solve the task of diameter, localization, and confidence of a basketball obtained from a calibrated monocular camera by estimating information about the basketball's diameter in pixels and the true ball's diameter in meters. VolleyVision combines the YOLO detection model and the DaSiamRPN single-object tracking method to form a volleyball tracking system that can quickly and efficiently locate and track volleyballs in videos. EKF reduces the missed ball rate by determining the candidate position of the soccer ball instead of trying to identify the soccer ball's position, calculating the distance between the soccer ball and the candidate soccer ball, removing incorrect soccer candidate positions through a threshold, and finally estimating the ball's position through the extended Kalman filter's location. Table 3 lists the results of our proposed method and other methods on the tracking result indicators AUC, \(P\), and \(P_{Norm}\) on the SportsTrack test set. As can be seen from the table, the method we proposed has the best overall tracking effect in soccer, basketball, and volleyball. It can be seen from the comparison results that on \(P_{Norm}\), our method has achieved the highest results in three types of balls (soccer, basketball, and volleyball), which are 87.9%, 84.7%, and 84.6%, respectively. In terms of AUC, our method is more than 2.9% higher than other comparison methods on the basketball test set; it is more than 4.4% higher than other methods on the volleyball test set. On \(P\), our method is more than 6% higher than other methods on the basketball test set; it is more than 3.7% higher than other methods on the volleyball test set. FPS is the abbreviation of frame per second, which represents the average inference speed of the model. In our model, the detection algorithm, trajectory matching algorithm, and trajectory correction algorithm are processed in stages. The FPS of our model in Table 3 is the overall processing speed calculated by adding the processing times of the three stages. Therefore, the overall processing speed of our model is slower compared with other SOTA comparison methods. Nonetheless, the speed of our tracking method, 28.2, is close to real-time processing standard (FPS = 30) and can be adapted to ball tracking in sports broadcast videos. To evaluate the overall performance of the proposed HMMATrack, we analyzed the detection results, as shown in Figure 6. Among them, P represents the detection precision, R represents the recall rate, and AP represents the average precision of IoU = 50, and the threshold is set to 0.3.