# Multiple Object Tracking Using Re-Identification Model with Attention Module
## 5 Conclusions  
MOT is one of the most-popular topics in computer vision. With the development of deep learning, research on computer vision has intensively evolved. We aimed to improve the performance by focusing on representative solutions to the MOT problem. In this paper, we proposed an effective utilization of image information in an online tracking method for real-time tracking. A vehicle re-ID dataset was used to track vehicles on roads. Validated methods such as fine-tuning were applied to distinguish objects with similar appearances. We also proposed the use of an appropriate sampler and mean vectors to address the occlusion problem arising from DBT and showed that they can help with reliably distinguishing between similar backgrounds. Furthermore, in the construction of the embedding network for re-identification, we proposed the use of a network with attention modules to enhance the performance while preserving the existing computational speed. By using an attention module, we achieved high performance while maintaining the size of the network model. We also demonstrated that our tracking system can show significantly improved tracking metrics compared with representative tracking systems. One of the factors that contributed to the significance of our results was the use of proven learning performance improvement methods. The use of fine-tuning, online batch samplers, and attention modules improved the performance of the embedding network. This showed that the performance improvement measures devised by various CNN-related studies can help improve the tracking performance. The results of DBT studies are proportional to the performance improvement achieved for detectors and embedding networks. Now that computer vision and CNNs are actively evolving with hardware performance, there is a clear possibility that multi-object tracking research can evolve. Furthermore, even if new deep learning techniques are developed, they can be easily applied to tracking systems. The scaling of deep learning algorithms is \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Model** & **Option** & **Recall\(\uparrow\)** & **Precision\(\uparrow\)** & **Frag\(\downarrow\)** & **MOTP\(\uparrow\)** & **IDs\(\downarrow\)** & **MOTA\(\downarrow\)** \\ \hline Proposed model & - & 0.89 & 0.85 & 27 & 0.66 & 31 & 0.71 \\ \hline Proposed model & Fine-tuning & 0.9 & 0.85 & 25 & 0.68 & 28 & 0.73 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of fine tuning. The arrows indicate better performance for each metric. (i.e., down arrow—lower is better). \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Detector** & **Embedding Network** & **Recall\(\uparrow\)** & **Precision\(\uparrow\)** & **Frag\(\downarrow\)** & **MOTP\(\uparrow\)** & **IDs\(\downarrow\)** & **MOTA\(\downarrow\)** \\ \hline \multirow{4}{*}{YOLOv3} & SORT & 0.87 & 0.84 & 36 & 0.66 & 71 & 0.69 \\  & GoogLeNet + FC & 0.9 & 0.83 & 33 & 0.65 & 46 & 0.72 \\ \cline{1-1}  & Triplet w/o att (ours) & 0.89 & 0.85 & 27 & 0.66 & 31 & 0.71 \\ \cline{1-1}  & Triplet (ours) & 0.9 & 0.85 & 25 & 0.68 & 28 & 0.73 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Experimental results for embedding networks. The arrows indicate better performance for each metric. (i.e., down arrow—lower is better). limited by hardware limitations. Current research trends focus on optimization methods for limited environments. Obviously, simply scaling the size of deep learning networks and datasets has the potential to produce good results. However, in this paper, we showed that performance can be improved even in constrained environments with well-known optimization methods and approaches. Advances in computer vision will continue to improve the performance of tracking systems, and tracking systems will evolve through various attempts. Conceptualization, W.-J.A., K.-S.K., M.-T.L. and T.-K.K. ; methodology, W.-J.A., K.-S.K., M.-T.L. and T.-K.K. ; software, W.-J.A., K.-S.K. and T.-K.K. ; validation, M.-T.L. and T.-K.K.K. ; formal analysis, [PERSON]-J.A., K.-S.K. and T.-K.K.