# Multi-Player Tracking for Multi-View Sports Videos with Improved K-Shortest Path Algorithm
## 4 Experiments
### Results
#### 4.3.2 Players Tracking Performance  
The tracking performance of our proposed KSP-AF method seriously depends on three components of the whole tracking system: players' 3D detection, feature extraction and fusion, and trajectory generation. In the following part, we focus on the latter two influence factors while the detection performance is beyond the scope of this paper. In particular, the KSP-AF tracking is mainly influenced by the proportion of available features. The more features we can extract, the better performance KSP-AF can reach. Ideally, once given the identity cues for each player in all frames, a trajectory for one player is just the detections with the same identity over a long sequence. However, it is impossible for us to extract all the features especially when the player are frequently occluded by each other. In order to analyze how much proportion of the identity features can ensure a satisfied tracking effect, we manually label all the identity information for each detected person in the court and gradually select a proportion of features as the input to test our algorithm. We have done three groups of experiments to verify the KSP-AF's performance. First, we only consider the color feature and test its improving performance when more color information is used. Second, we gradually increase both of the color feature and number cues to explore the variation trend of the tracking performance. These two experiments are done on the publicly APIDIS dataset. Finally, to further demonstrate the effectiveness of our method, we use our own STU dataset, totaling 10 periods of sequences whose lengths vary from 600 to 4400 frames. **(1) Tracking experiments with jersey color only(APIDIS dataset). **  
We first apply the original KSP tracking approach without considering any appearance feature in the 1500-frames APIDIS dataset, and we can obtain as many as 14 trajectories with the MOTA and GMOTA scores being 0.83 and 0.71 respectively, as shown in Figure 9 (0%Color). Then, as we gradually exert more jersey color information into our KSP-AP tracking framework, the tracking performance improves very little or even degrade, as shown in Figure 9. Both the MOTA and GMOTA scores do not increase a lot until more than 70% of the color information can be available. This is because players often wear the same uniforms when they belong to the same team, and thus integrating only the jersey color into the tracking framework cannot completely distinguish players. Anyway, when more than 70% of color feature can be available over the sequence, the tracking performance of KSP-AF would be obviously improved compared with the original KSP method (0%Color). **(2) Tracking experiments with both jersey color and jersey number (APIDIS dataset). **  
Generally, we can have access to more jersey color than jersey number. In both APIDIS and STU datasets, we can commonly extract around 50% to 80% of the jersey color and 40% to 60% of the jersey number based on some advanced extracting methods [51; 52]. The available proportions of these two features depend on several factors, including the image resolution, light conditions, and extracting approach. More features in the tracking process would result an obviously better tracking performance, as shown in Figure 10. More detailed comparison results can be seen in Table 1.  
Figure 10: The tracking performance of KSP-AF is gradually improved when more and more jersey colors and jersey numbers are given: (1) 0%Color+%0 Num corresponds the original KSP; (2) under KSP-AF, both the MOTA and GMOTA scores gradually increase when more appearance information (e.g., jersey color and jersey number) can be extracted; (3) this experiment is done with APIDIS dataset. Figure 9: Using only jersey color information cannot largely improve the tracking performance for KSP-AF: (1) 0%Color corresponds the original KSP; (2) both MOTA and GMOTA scores are not largely increased with the escalation of jersey color cues; (3) this experiment is implemented in APIDIS dataset.