# Dynamic Collaborative Optimization Method for Real-Time Multi-Object Tracking
## 4 Experimental Results and Analysis
### Experimental Setup  
#### 4.1.1 Datasets and Evaluation Metrics  
The experimental evaluation was conducted on the following publicly available datasets:  
* MOT17 [26]: A more extensive dataset with 14 sequences, including varying lighting conditions, camera movements, and crowded environments.
* MOT20 [27]: A dataset focused on extremely crowded scenarios, with an average density of 246 people per frame.
* VisDrone-MOT [6; 28]: A dataset captured from a drone's perspective, containing numerous small targets with significant size variations.  
The evaluation employed the CLEAR MOT metrics [29], which include several important indicators for assessing multi-object tracking performance. The specific calculation formulas are as follows:  
Multiple Object Tracking Accuracy (MOTA)  
MOTA comprehensively considers the impact of false positives (FP), false negatives (FN), and identity switches (IDs) on tracking accuracy, calculated as follows:  
\[MOTA=1-\frac{\sum_{t}(FP_{t}+FN_{t}+IDs_{t})}{\sum_{t}GT_{t}} \tag{20}\]  
where \(t\) represents the frame index, \(FP_{t}\) is the number of false positives at frame \(t\), \(FN_{t}\) is the number of false negatives at frame \(t\), \(IDs_{t}\) is the number of identity switches at frame \(t\), and \(GT_{t}\) is the number of ground truth targets at frame \(t\). MOTA ranges within \((-\infty,1]\), with values closer to 1 indicating better tracking performance.  
Identification F1 Score (IDF1)  
IDF1 measures the tracker's performance in correctly identifying target identities, combining true positives for identities (\(TP_{t}\)), false positives (\(FP\)), and identity switches (\(IDs\)), calculated as follows:  
\[IDF1=2\times\frac{\sum_{t}TP_{id,t}}{\sum_{t}(2 TP_{id,t}+FP_{t}+IDs_{t})} \tag{21}\]  
where \(TP_{id,t}\) is the number of correctly identified target identities at frame \(t\). IDF1 ranges within \([0,1]\), with higher values indicating better performance in maintaining target identity consistency.  
Higher Order Tracking Accuracy (HOTA)  
HOTA is a more comprehensive metric that simultaneously considers detection precision and identity association accuracy by combining location accuracy (LA) and identity accuracy (IA). First, location accuracy \(LA\) is defined as follows:  
\[LA=\frac{\sum_{t}\sum_{i\in GT_{t}\cap Det_{t}}{volU(gt_{i},det_{i})}}{\sum_{t }|GT_{t}\cap Det_{t}|} \tag{22}\]where \(GT_{t}\) is the set of ground truth targets at frame \(t\), \(Det_{t}\) is the set of detected targets at frame \(t\); \(gt_{i}\) and \(det_{i}\) are the bounding boxes of the ground truth and detected targets at frame \(t\), respectively; and \(IoU(gt_{i},det_{i})\) is their intersection over union. Next, identity accuracy \(IA\) is defined as follows:  
\[IA=\frac{\sum_{t}TP_{id,t}}{\sum_{t}(TP_{id,t}+\frac{1}{2}(FP_{t}+ID_{st}))} \tag{23}\]  
Finally, HOTA is calculated as follows:  
\[HOTA=\sqrt{LA\times IA} \tag{24}\]  
HOTA ranges within \([0,1]\), with higher values indicating better overall performance in both detection and identity association.  
These key metrics enable a comprehensive assessment of the tracker's performance in terms of identity retention capability and trajectory integrity.