# Dynamic Collaborative Optimization Method for Real-Time Multi-Object Tracking
## 4 Experimental Results and Analysis
### Ablation Studies
#### 4.2.3 Memory Pool Depth  
The memory pool depth determines the amount of historical information that the model can access. A deeper memory pool allows the model to capture long -term dependencies and patterns in the tracking process. In multi-object tracking, objects may reappear after long occlusions, and having a sufficient memory depth enables the model to recall the object's appearance and motion patterns from previous encounters. This is crucial for maintaining consistent object identities over time.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **MOTA** & **IDF1** & **IDs** & **FPS** \\ \hline Baseline & 19.8 & 38.5 & 568 & 22.1 \\ +MS-FAE & 22.3 & 41.6 & 502 & 21.4 \\ +CFAM & 35.6 & 45.7 & 379 & 20.7 \\ +DMM & 38.6 & 46.4 & 268 & 19.9 \\ +BDDM (Ours) & 39.3 & 48.2 & 215 & 19.6 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Ablation study on the VisDrone dataset.  
Figure 4: Sensitivity analysis of the SAAM parameters. The yellow line in the figure indicates the MOTA values for different parameter \(k\) conditions, the blue line indicates the MOTA values for different parameter \(S_{0}\), and the red line indicates the corresponding parameter position when the maximum MOTA was achieved.  
This ablation study systematically evaluates the impact of memory pool depth on target tracking performance, validating both the effectiveness of the dynamic memory recall mechanism and the rationality of its parameter settings. As shown in Table 2, tracking performance exhibits significant improvement as the memory pool depth increases from 1 to 5, followed by a gradual plateau in performance gains.  
As shown in Table 2, the model exhibits different characteristics with varying memory pool depths. In terms of tracking precision, when the depth increases from 1 to 3, the MOTA metric improves from 37.4% to 39.3%, IDF1 rises from 46.7% to 48.2%, and the number of IDs significantly decreases from 368 to 215. These results indicate that incorporating multi-frame historical features can mitigate feature degradation caused by short-term occlusion, while the weighted fusion of features from the recent three frames substantially enhances target representation robustness. However, there are diminishing marginal returns as follows: performance improvements notably narrow when the depth exceeds 3, with depth 5 showing only 0.3% MOTA and 0.4% IDF1 improvements compared to depth 3. This suggests that a 3-frame memory window effectively covers typical short-term occlusion scenarios, and additional historical feature information provides limited benefits. Regarding computational efficiency, although increasing memory pool depth gradually reduces the frame rate from 20.1 FPS to 19.2 FPS, the decrease is relatively modest, with each additional depth layer reducing approximately 0.2-0.3 FPS. This indicates that the designed weighted computation mechanism effectively controls computational complexity, maintaining real-time processing capability at 19.6 FPS with a depth of 3.  
The study ultimately determines that a memory pool depth of 3 achieves the optimal balance among tracking precision (MOTA 39.3%), identity continuity (215 IDs), and computational efficiency (19.6 FPS). Compared to the baseline method (depth of 1), this configuration reduces the number of IDs by 31.6%, effectively validating the efficacy of the dynamic memory update rules and exponential decay weight design. The results demonstrate that the proposed approach significantly enhances target association reliability under occlusion scenarios without substantially increasing computational burden.