# Dynamic Collaborative Optimization Method for Real-Time Multi-Object Tracking
## 4 Experimental Results and Analysis
### Performance Evaluation
#### 4.3.2 Small Object Tracking Performance  
Given this study's particular focus on small object tracking, we conducted a dedicated evaluation using the VisDrone-MOT dataset, with the results presented in Table 7.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **HOTA** & **MOTA** & **IDF1** & **IDs** & **FPS** \\ \hline ByteTrack [18] & 61.3 & 77.8 & 75.2 & 1223 & 17.5 \\ Fair-MOT [30] & 54.6 & 61.8 & 67.3 & 5243 & 13.2 \\ StrongSORT++ [19] & 62.6 & 73.8 & 77.0 & 770 & 1.4 \\ SORT [8] & 36.1 & 42.7 & 45.1 & 4470 & 57.3 \\ DeepSORT [7] & 57.1 & 71.8 & 69.6 & 1418 & 3.2 \\ MAATrack [32] & 57.3 & 73.9 & 71.2 & 1331 & 14.7 \\ BoostTrack++ [33] & 66.4 & 77.7 & 82.0 & 762 & 2.1 \\ Ours & 61.5 & 77.1 & 76.5 & 1162 & 27.6 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Performance comparison on MOT20.  
Figure 6: Performance-Speed Trade-offs Comparison on The MOT20 Dataset.  
The experimental results demonstrate that our proposed method achieves optimal performance in both MOTA (39.3) and FPS (14.6), significantly outperforming other approaches. Additionally, it exhibits exceptional performance in IDF1 (48.2) and ID (215) metrics, particularly surpassing most comparative methods in identity consistency. Compared to CMOT, our method maintains a high IDF1 while substantially reducing the number of ID switches, and it demonstrates superior computational efficiency over DeepSORT and Flow-Tracker. Overall, our approach achieves a better balance among tracking accuracy, identity stability, and computational efficiency, providing an efficient and robust solution for multi-object tracking tasks.  
As shown in Figure 7, the image sequence depicts a cyclist temporarily obscured by a bus before reappearing. Although UAV-SIFT detects the cyclist in the process, their ID changes before and after the occlusion, and our proposed method not only succeeds in maintaining continuous detection, but also preserves the consistent ID of the cyclist before and after the occlusion.  
When evaluating small object tracking performance using the VisDrone -MOT dataset, our method clearly outperforms others, with high MOTA and IDF1 scores indicating its effectiveness in detecting small objects and maintaining identity consistency. Compared to Deepsort, the Multi-Scale Feature Adaptive Enhancement (MS-FAE) module in our method is a major advantage. It combines spatial details and semantic information through multi-scale feature pyramid fusion, which is crucial for small objects with indistinct single-scale features. Its small object adaptive attention mechanism also better highlights small object features. In terms of identity stability, our method has far fewer ID switches, mainly due to the Cross-Frame Association Module (CFAM). The CFAM's grouped cross-attention captures global semantic associations, and its memory recall mechanism maintains object identity during occlusions, as demonstrated by the successful tracking of a cyclist during occlusion while UAV-SIFT failed. Regarding computational efficiency, our method runs at 14.6 FPS, faster than some counterparts like Flow-Tracker and Deepsort. This is achieved through the optimized design of modules like CFAM, whose grouped cross-attention reduces computational complexity, enhancing both feature representation and calculation speed. Overall, the combination of innovative modules such as MS-FAE and CFAM, along with well-tuned hyperparameters, gives our method an edge in detection accuracy, identity stability, and computational efficiency for small object tracking.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Method** & **MOTA** & **IDF1** & **IDs** & **FPS** \\ \hline Deepsort [7] & 10.1 & 38.3 & 590 & 2.9 \\ IoU Tracker [34] & 12.6 & 38.3 & 576 & 6.3 \\ Flow-Tracker [35] & 26.4 & 41.9 & 127 & 2.6 \\ GOC-ECO [36] & 36.9 & 46.5 & 354 & 5.6 \\ CMOT [37] & 31.5 & 51.3 & 789 & 7.3 \\ UAV-SIFT [38] & 38.7 & 47.3 & 304 & 10.7 \\ Ours & 39.3 & 48.2 & 215 & 14.6 \\ \hline \end{tabular}
\end{table}
Table 7: Small object tracking performance on the VisDrone dataset.