# Dynamic Collaborative Optimization Method for Real-Time Multi-Object Tracking
## 4 Experimental Results and Analysis
### Performance Evaluation  
#### 4.3.1 Comparative Analysis with State-of-the-Art Methods  
We conducted a comprehensive comparison between our proposed method and contemporary state-of-the-art tracking algorithms, and the results are as follows. The experimental results on the MOT17 test set are presented in Table 5.  
As illustrated in Figure 5 and Table 5, the proposed model demonstrates significant advantages when compared with ByteTrack, FairMOT, and other models on the MOT17 \begin{table}
\begin{tabular}{c c c c} \hline
**Fusion Method** & **MOTA** & **IDF1** & **IDs** \\ \hline Appearance Only & 77.5 & 75.3 & 1653 \\ Motion Only & 76.8 & 71.4 & 1872 \\ Fixed Weight (50:50) & 78.3 & 77.6 & 1385 \\ Fixed Weight (70:30) & 78.9 & 78.1 & 1241 \\ Our BDDM (Dynamic) & 79.8 & 79.4 & 1134 \\ \hline \end{tabular}
\end{table} 
Table 4: Comparison of fusion methods on MOT17. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Method** & **HOTA** & **MOTA** & **IDF1** & **IDs** & **FPS** \\ \hline ByteTrack [18] & 63.1 & 80.3 & 77.3 & 2196 & 29.1 \\ Fair-MOT [30] & 59.3 & 73.7 & 72.3 & 3303 & 25.2 \\ StrongSORT++ [19] & 64.4 & 79.6 & 79.5 & 1194 & 7.0 \\ SORT [8] & 34 & 43.1 & 39.8 & 4853 & 141.3 \\ DeepSORT [7] & 61.2 & 78.0 & 74.5 & 1821 & 13.8 \\ TransTrack [20] & 54.1 & 75.2 & 63.5 & 3603 & 58.2 \\ CorrTracker [31] & 60.7 & 76.5 & 73.6 & 3369 & 14.9 \\ Ours & 63.7 & 79.8 & 79.4 & 1134 & 61.4 \\ \hline \end{tabular}
\end{table} 
Table 5: Performance comparison on MOT17. dataset, while ByteTrack achieves competitive performance in detection accuracy (MOTA 80.3), it shows limitations in identity preservation and association in complex scenarios, evidenced by its higher number of ID switches (2196) compared to our approach (1134). Similarly, FairMOT, despite its integrated detection-tracking framework, struggles with maintaining identity consistency (3303 ID switches) and achieves lower comprehensive tracking metrics (HOTA 59.3, IDF1 72.3). These baseline methods face challenges in scenarios involving occlusions, crowded scenes, and non-linear motion patterns. In terms of accuracy-related metrics, our model achieves a HOTA score of 63.7 (compared to ByteTrack's 63.1 and FairMOT's 59.3), a MOTA score of 79.8 (versus ByteTrack's 80.3 and FairMOT's 73.7), and an IDF1 score of 79.5 (surpassing ByteTrack's 77.3 and FairMOT's 72.3). These results indicate superior performance in comprehensive tracking, object detection, and identity recognition accuracy, enabling precise detection, the tracking of multiple targets, and maintenance of correct identity recognition. Regarding identity stability, our model records only 1134 ID switches, substantially lower than ByteTrack's 2196 and FairMOT's 3303, demonstrating robust continuous tracking capability in complex scenarios while reducing identity switches and enhancing tracking reliability. In terms of real-time performance measured by FPS, our model operates at 61.4 FPS, significantly outperforming ByteTrack (29.1 FPS), FairMOT (25.2 FPS), and StrongSORT (7.0 FPS), showcasing superior efficiency in processing video frames and real-time applications. As illustrated in Figure 6 and Table 6, the proposed model demonstrates exceptional performance in the more challenging MOT20 dataset scenarios. In terms of accuracy metrics, it achieves a HOTA score of 61.5, MOTA of 76.1, and IDF1 of 76.5, maintaining high detection and tracking precision compared to FairMOT (HOTA 54.6, MOTA 61.8, and IDF1 67.3) and StrongSORT (HOTA 62.6, MOTA 73.8, and IDF1 77.0). Regarding identity stability, our model records only 1562 ID switches, substantially lower than FairMOT's 5243, demonstrating superior continuous tracking capability in complex backgrounds with frequent target interactions. For real-time performance, our model operates at 23.6 FPS, outperforming both FairMOT (13.2 FPS) and StrongSORT (1.4 FPS), proving its ability to balance detection accuracy and real-time processing in complex scenarios while achieving efficient video stream processing with high-precision tracking.