# Single-Task Joint Learning Model for an Online Multi-Object Tracking Framework
## 3 Experiments
### Evaluation of STJL Performance  
STJL is a training strategy that utilizes multiple datasets to enhance the model's generalization capabilities for broader applications. In this section, the constraints of training with a singular dataset are examined, and the efficacy of the STJL method is evaluated. Experiments using three datasets are detailed: CUHK03 ('c'), Market-1501 ('m'), and DukeMTMC-reID ('d'). All images were resized to 128 \(\times\) 64 pixels, as listed in Table 1. The training method used was cosine metric learning. Models were first trained using individual datasets and subsequently evaluated across all three datasets using CMC, with an emphasis on Rank 1 accuracy. Table 1 shows the image counts for training. The results in Figure 5 confirm that the single-dataset models excelled only on their corresponding test set, revealing a lack of generalization. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{2}{c}{**Number of Images**} & \multicolumn{2}{c}{**Number of IDs**} \\ \cline{2-5}  & **Training Set** & **Test Set** & **Training Set** & **Test Set** \\ \hline CUHK03 & 15,315 & 3309 & 900 & 100 \\ Market-1501 & 12,936 & 19,732 & 750 & 751 \\ DukeMTMC-reID & 16,522 & 17,661 & 702 & 1110 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Number of images in each dataset. Figure 4: Temporal diagram for pedestrian re-ID testing. Figure 5: Rank 1 accuracy comparison: (**a**) single dataset models, (**b**) multi-dataset models trained on Market-1501. Single- and multi-dataset training strategies were contrasted, using Market-1501 as a baseline, with all multi-dataset models incorporating them for training. The total number of training images was the sum of those in each dataset. Models were named based on the datasets used for training; for example, the model trained on Market-1501 and DukeMTMC-reID was named'md' and the one using all three datasets was 'cmd'. Four models:'m','md', 'cm', and 'cmd', were contrasted, as depicted in Figure 5b. The results indicate that multi-dataset models outperformed the single-dataset model'm'. Among the dual-dataset models,'md' and 'cm' saw Rank 1 accuracy increases of approximately 0.8% and 0.6%, respectively, while the 'cmd' model using all three datasets improved by 1%. Subsequently, the STJL approach was continued with multiple datasets, centering on the performance of these multi-dataset models across varied test datasets. Four distinct multi-dataset models: 'cm', 'cd','md', and 'cmd', were trained and assessed on three datasets. The results are shown in Figure 6a. The results indicate that models trained on two datasets performed worse on unseen datasets, whereas the model trained on all three datasets ('cmd') showed good performance across all test datasets. Moreover, 'cmd' outperformed the two-dataset models, with a 0.4% higher accuracy on Market-1501 compared with 'cm', and a 1.5% higher accuracy on CUHK03 compared with 'cd'. In summary, our experiments showed that using a single dataset was insufficient for robust pedestrian re-identification. For example, Market-1501 lacked the scope to train the model for occlusions, while adding DukeMTMC-reID improved the performance by 0.8%. This supports the notion that diverse datasets not only enrich the training data but also address specific challenges. Hence, the 'cmd' model was chosen for subsequent endeavors. Building upon prior training approaches, STJL was executed using CUHK03, Market-1501, and DukeMTMC-reID as the training datasets. Two learning methodologies were contrasted; the initial one employed the conventional softmax classifier (denoted as \"softmax\"), and the latter leveraged cosine metric learning (termed \"cosine softmax\"). The primary evaluation metric was centered on the CMC-Rank 1 accuracy. The model comparison results in Figure 6b show that the models trained with the standard softmax classifier performed poorly on all three datasets, indicating their inability to learn generalizable features.