# Ball Tracking Based on Multiscale Feature Enhancement and Cooperative Trajectory Matching
## 4 Experiments
### Data Set and Evaluation
#### 4.1.2 Data Partition  
In the detection model training, we adopt the joint data set training mode and use the public SoccerDB [38], APIDIS [39], and VolleyballVision [40] data sets as auxiliary training. SoccerDB [38] is a comprehensive soccer scene data set proposed by Xinhua Zhiyun that can be used for various tasks such as object detection, action recognition, temporal segmentation, and highlight detection [5]. The images used for the soccer detection task are crawled from the Internet or sampled from competition videos. There are 45,732 frames of images belonging to the competition videos. In order to ensure the efficiency of training and the relative balance of sample distribution, SoccerDB is eliminated during training. There are no objects or samples containing sparse objects, and there are 40,615 frames of images used for training after screening. The advantage of SoccerDB is that it is large in scale and includes the detection and annotation of soccer balls, players, and goals. However, the disadvantage is that all annotation data are generated by detectors rather than manual annotations, resulting in a large number of annotation errors. In addition, the image quality is relatively poor. APIDIS [39] is a basketball scene data set provided by the UCLouvain image and signal processing team of ICTEAM in Belgium that can be used for event detection, object detection, and tracking. The video sequences in the data set come from the European APIDIS project. The data set used for the available basketball tracking task contains 12,907 frames with a video resolution of 1600 \(\times\) 1200. The advantage of the APIDIS data set is that the data set annotations are all manual annotations and are relatively accurate. The disadvantage is that all video clips come from the same basketball competition, and the scene is relatively singular. VolleyballVision [40] is a volleyball detection data set provided by the Roboflow computer vision platform and Dong-A University. The images used to detect come from public images on the Internet. There are 25,239 images containing volleyball, 17,679 training sets, 2539 test set images, and 5021 verification set images. Annotation information is provided, but the verification set image is the source. The images in the training set and test set were processed, so we only selected a total of 20,218 images in the training set and test set in the original VolleyballVision as the volleyball detection auxiliary training set. The advantage of VolleyballVision is that it is large in scale, including indoor, outdoor, hard volleyball, air volleyball, beach volleyball, and other scenes. The disadvantage is that all images are single images and not derived from video sequences. There is no continuity or correlation between the images in the data set, and it can only be used for volleyball detection tasks without tracking annotations.  
The division of the joint training set and test set used for detection is shown in Table 2. In addition, this paper also randomly divides 25% of the entire set as a detection training set for model search and ablation experiments, while the test set remains unchanged.