# A Novel Dataset for Multi-View Multi-Player Tracking in Soccer Scenarios
## 4 Benchmark
### Evaluated Methods  
For multiple object tracking, we provided results of the four state-of-the-art methods, so that we can better observed the challenges in our dataset in these evaluations  
Figure 5: Comparison between our dataset and WildTrack dataset. (**a**) Track length comparison. (**b**) Motion speed comparison.  
**TRACTA**[68] (tracking by tracklet-to-target assignment) treats multi-view multi-object tracking as a cross-camera tracklet matching problem. There are four major modules in the method: (1) local tracklet construction, (2) tracklet similarity calculation, (3) cross-camera tracklet connection, and (4) global trajectory construction. The TRACTA system employs the Restricted Non-negative Matrix Factorization (RNMF) algorithm to determine the optimal assignment solution that adheres to practical constraints. This enabled TRACTA to mitigate tracking errors caused by occlusions and missed detections in local tracklets and generate a comprehensive global trajectory for each target across all cameras.  
**MvMHAT**[73] proposed a self-supervised learning approach to address the MVMOT challenge. The researchers started by selecting several frames from different views and using convolutional neural networks (CNNs) to extract the embedding features for each subject. Next, they introduced a spatial-temporal association network to consider both the temporal relationships over time and the spatial relationships across views. This generates a matching matrix which is self-supervised by symmetric similarity and transitive similarity losses. During the implementation stage, they utilized a novel joint tracking and association strategy to tackle the problem.  
**DAN4 Ass**[74] approached the MVMOT problem for \(C>2\) views as a constraint optimization challenge and created a novel end-to-end solution. This solution consists of two components: building the affinity matrix and assigning multi-view multi-cliques. The affinity network calculates the similarity between subjects detected in different views, while the Deep Assignment Network represents the multi-view constraints as differentiable loss functions during unsupervised training. DAN integrated four specific constraints into the model and combined image feature extraction, affinity matrix calculation, and assignment optimization into one unified framework for joint training.  
**MVFlow**[76] proposed a weakly supervised approach to detect people flow given only detection supervision. The approach starts by incorporating a detection network that predicts people flow in a weakly supervised manner. The existing association algorithms are then modified to generate clear tracks using the predicted flows. This is followed by transforming two consecutive sets of multi-view frames into human flow using the proposed multi-view prediction model. The human flow is then utilized to rebuild detection heatmaps. The flow-based framework benefits from the temporal consistency across video frames and enforces consistency in scale and motion over various viewpoints.  
To ensure the fairness of the experiment, we used the open source code provided by the authors to train, and used the unified evaluation method mentioned above to evaluate. After getting the results close enough to the results in the authors' papers, we used the same training methods to train and test on each subset of our dataset separately.