## 2 Materials and Methods
### Evaluation Metrics  
The evaluation metrics for Multi-Object Tracking were used to assess the performance of tracking algorithms [13]. These metrics reflected the algorithm's performance regarding tracking accuracy, persistence, and identification capabilities. Below, we introduce the MOT evaluation metrics used in this study, along with their meanings and formulas:  
1. Multi-Object Tracking Accuracy (MOTA)  
MOTA [14] comprehensively considers errors such as false positives, missed detections, and ID switches, indicating overall tracking accuracy.  
\[\text{MOTA}=1-\frac{\sum_{\text{t}}(FN_{\text{t}}+FP_{\text{t}}+ IDSW_{\text{t}})}{\sum_{\text{t}}GT_{\text{t}}} \tag{1}\]  
In this context, _FNs_ (false negatives) represent the number of missed detections, indicating the number of targets that exist but were not detected by the system. _FPs_ (false positives) represent the number of false positives, indicating the number of non-targets the system mistakenly marked as targets. _IDSWs_ (identity switches)represent the number of identity switches, with a smaller number indicating better performance. _GT_ represents the number of real targets, that is, the number of targets present in each frame.  
2. Identity F1 Score (IDF1)  
IDF1 is specifically used to measure the performance of tracking systems in maintaining the consistency of target identities. IDF1 [15] was introduced by [PERSON] et al. in 2016 and is used to assess the ability of tracking systems to correctly identify and associate target identities throughout an entire video sequence. Unlike the MOTA metric, which focuses on the accuracy and precision of tracking, IDF1 concentrates more on the accuracy of identity preservation. The IDF1 score is based on the harmonic mean of precision and recall, but it calculates the precision and recall of tracking identities rather than tracking locations. Here, precision is defined as the ratio of times the system correctly associates target identities to the total number of times the system associates target identities. Recall is defined as the ratio of times the system correctly associates target identities to the number of actual identity switches in the video sequence. The calculation of IDF1 can be divided into three steps:  
1. Precision \[Precision=\frac{TP}{TP+FP}\] (2)  
In this context, _TPs_ (true positives) represent the number of correctly associated target identities and _FPs_ (false positives) represent the number of incorrectly associated target identities.
2. Recall  
In this context, _FNs_ (false negatives) represent the number of missed identity switches, indicating the number of times target identities were changed but not detected by the system.
3. Calculating IDF1  
\[\text{IDF1}=2\times\frac{Precision\times Recall}{Precision+Recall} \tag{3}\]The IDF1 score reflects a tracking system's ability to maintain the consistency of target identities throughout the tracking process. A high IDF1 score means that the tracking system can effectively track individual targets and maintain their identities correctly during interactions or occlusions among targets. This is particularly important in applications where long-term tracking and consistency of identity are required, such as video surveillance, sports analysis, and social behavior research.  
These evaluation metrics provide a comprehensive performance assessment for multi-object tracking algorithms, helping researchers to identify the strengths and weaknesses of the algorithms and guiding the optimization and improvement of these algorithms.