# Ball Tracking Based on Multiscale Feature Enhancement and Cooperative Trajectory Matching
## 4 Experiments
### Performance Comparison with Other Ball-Tracking Methods
As can be seen from the figure, our detection algorithm exhibits higher accuracy; however, its complete recall is relatively low, indicating that there is a large number of undetected instances in the algorithm. The framework we adopt belongs to the detection-based tracking category, which may explain the insufficient tracking performance. Going forward, our focus will be on improving the recall of our detection algorithms. Figure 7 shows the advantages of our method for ball tracking in the presence of occlusion, motion blur, and blend in the background. Figure 7 shows an example of tracking results comparing our method with other methods. We selected three situations with tracking difficulties: occlusion, motion blur, and background blending. In frames 29 to 36 of the fourth clip of SportsTrack's basketball test set, the player's hands obscured the basketball. (a) and (b) in Figure 7 are screenshots of the basketball part of these eight consecutive images. Other methods did not track the basketball, as shown in Figure 7a. In contrast, our method tracks all basketballs with hand occlusion. In frames 352 to 359 of the first clip of SportsTrack's soccer test set, the soccer ball blends into the background of the auditorium. Figure 7c,d is screenshots of the soccer part of these eight consecutive images. Other methods did not track the soccer player, as shown in Figure 7c. In contrast, our method tracks the entire ball in a situation where the ball is very similar to the head in the audience, as shown in Figure 7d. In the first clip of SportsTrack's volleyball test set, motion blur occurs from frames 97 to 104 due to the excessive speed of the volleyball. Figure 7e,f is screenshots of the volleyball part of these eight consecutive images. The blurred volleyball was not tracked by other methods, as shown in Figure 7e. In contrast, our method tracks all volleyballs, as shown in Figure 7f. \begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**Soccer**} & \multicolumn{4}{c}{**Basketball**} & \multicolumn{4}{c}{**Volleyball**} \\  & **AUC** & \(P\) & \(P_{Norm}\) & **AUC** & \(P\) & \(P_{Norm}\) & **AUC** & \(P\) & \(P_{Norm}\) & **FPS** \\ \hline UNINEXT & **78.5** & **86.1** & 87.5 & 63.7 & 69.0 & 71.2 & 61.6 & 67.7 & 70.3 & 30.3 \\ OSTrack & 76.1 & 81.6 & 84.2 & 71.6 & 77.5 & 79.5 & 72.3 & 79.0 & 81.9 & **69.7** \\ STACK & 66.5 & 72.9 & 75.0 & 57.9 & 63.4 & 65.5 & 56.7 & 62.2 & 64.8 & 35.6 \\ DeepSport & 63.7 & 70.0 & 72.8 & 72.0 & 77.9 & 80.4 & 60.7 & 66.7 & 69.2 & 28.6 \\ VolleyVision & 67.0 & 73.0 & 75.1 & 59.8 & 65.1 & 67.2 & 70.4 & 76.2 & 78.9 & 57.3 \\ EKF & 64.4 & 70.1 & 72.3 & 66.8 & 72.6 & 75.2 & 57.3 & 64.0 & 66.3 & 16.4 \\ Ours & 78.3 & 85.5 & **87.9** & **74.9** & **83.9** & **84.7** & **76.7** & **82.7** & **84.6** & 28.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison results with other state-of-the-art methods on AUC, P, and \(P_{Norm}\) in the SportsTrack test set including soccer, basketball, and volleyball. The best results are in bold. FPS is the average inference speed. Figure 6: MNet-based detection results on P, R, AP in SportsTrack test data.