# Dynamic Collaborative Optimization Method for Real-Time Multi-Object Tracking
## 4 Experimental Results and Analysis
### Ablation Studies
#### 4.2.4 Ablation Study on Dynamic Motion Model Components  
To evaluate the effectiveness of our Dynamic Motion Model and quantify the contribution of each proposed component, we conducted a comprehensive ablation study comparing the traditional Kalman filter with various configurations of our DMM. Table 3 presents the experimental results on the MOT17 dataset. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Memory Pool Depth** & **MOTA** & **IDF1** & **IDs** & **FPS** \\ \hline
1 & 37.4 & 46.7 & 318 & 20.1 \\
2 & 38.5 & 47.6 & 243 & 19.8 \\
3 & 39.3 & 48.2 & 215 & 19.6 \\
4 & 39.4 & 48.5 & 201 & 19.4 \\
5 & 39.6 & 48.6 & 197 & 19.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Impact of memory pool depth on tracking performance. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Configuration** & **MOTA** & **IDF1** & **IDs** & **Pred. Error (px)** \\ \hline Traditional Kalman & 77.3 & 75.4 & 1586 & 12.6 \\ +Enhanced State Transition (EST) & 78.2 & 76.9 & 1387 & 9.5 \\ +Multi-Trajectory Hypothesis (MTH) & 78.7 & 77.8 & 1256 & 8.8 \\ +Data-Driven Noise Adjustment (DNA) & 79.8 & 79.4 & 1134 & 7.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Ablation study of DMM components on MOT17. The results demonstrate the progressive performance improvement achieved by each component of our DMM. Starting from the traditional Kalman filter as a baseline, we observe the following contributions:  
Enhanced State Transition (EST):By incorporating acceleration components into the state vector and implementing a velocity dampening mechanism, this component reduces prediction error by 24.6% (from 12.6 to 9.5 pixels) and decreases identity switches by 12.5%. The improvements are particularly significant in sequences with non-uniform motion patterns, where the standard linear model's assumptions are frequently violated. The expanded state representation enables more accurate modeling of complex trajectories, such as those with varying acceleration and sharp turns. Multi-Trajectory Hypothesis (MTH): Building on the enhanced state transition, the addition of multi-trajectory hypothesis generation further improves prediction accuracy and tracking stability. This component maintains multiple potential trajectory paths during occlusions, reducing ID switches by an additional 9.4% and prediction error by 7.4%. Analysis of difficult tracking scenarios reveals that MTH is particularly effective in recovering targets after prolonged occlusions (>10 frames), where traditional single-hypothesis approaches often fail to re-establish correct identities. Data-Driven Noise Adjustment (DNA): The final component, which dynamically adjusts process and observation noise matrices based on motion characteristics and detection quality, provides the most substantial improvement in tracking identity consistency. When integrated with the previous components, it further reduces ID switches by 9.7% and prediction error by 17.0%. This adaptive approach proves especially valuable in heterogeneous scenes where target behavior varies significantly, allowing the filter to automatically adjust its reliance on prediction versus observation based on contextual cues. The complete DMM configuration, incorporating all three components, achieves a 28.5% reduction in identity switches and a 42.1% reduction in prediction error compared to the traditional Kalman filter. These results validate our design hypothesis that addressing specific limitations of conventional motion models--namely, limited state representation, single-trajectory prediction, and static noise parameters--significantly enhancing tracking performance in complex scenarios. Further analysis of the sequence-specific results reveals that the combination of these components produces synergistic effects beyond their individual contributions. For example, the enhanced state transition works particularly well with the data-driven noise adjustment in handling abrupt motion changes, while the multi-trajectory hypothesis complements both components during occlusion handling.