# Smart Logistics Warehouse Moving-Object Tracking Based on YOLOv5 and DeepSORT
## 5 Discussion  
In the absence of relevant research and open-source datasets for logistics packages, this study has constructed a logistics warehouse image dataset, which was applied to the combined algorithm of YOLOv5 and DeepSORT to achieve object tracking within logistics warehouse scenarios. The combined algorithm was numerically evaluated by the MOT Challenge evaluation metrics. The study has achieved good tracking performance and provided a validation approach for the effectiveness of eliminating manual video surveillance  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Times** & **MOTA** & **MOTP** & **Rcll** & **MT** & **ML** & **IDF1** & **IDP** & **IDR** & **IDSW** & **FP** & **FN** & **FM** \\ \hline First & 63.7\% & 0.131 & 86.6\% & 23 & 0 & 75.5\% & 72.4\% & 78.8\% & 56 & 1858 & 1120 & 129 \\ Second & 75.8\% & 0.118 & 87.8\% & 25 & 0 & 83.8\% & 84.0\% & 83.5\% & 26 & 973 & 1022 & 96 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The first and second tracking results.  
Figure 18: Transfer learning for DeepSORT.  
in logistics scenarios. This accomplishment signifies an effective smart monitoring approach for \"Sustainable, Human-Centered/Centric and Resilient Logistic Systems Design and Management\".  
The combination of YOLOv5 and DeepSORT is specifically designed to cater to human-centric logistics scenarios, where real-time object detection and multi-object tracking are essential. The following are some applications:  
* One primary application is the determination of warehouse zone occupancy. By accurately tracking the movement of objects in different zones, real-time occupancy data could be easily garnered, which optimizes inventory management and streamlines logistics. Additionally, it facilitates the prediction of space utilization trends, enabling the proactive allocation of resources.
* The method contributes to safety protocols within warehouses. By continually tracking the trajectories of objects and employees, potential hazards and unsafe practices can be identified promptly, minimizing the risk of accidents.
* The method's versatility extends to inventory management, offering insights into warehouse stock movement and depletion rates, and preventing overstock situations.  
In conclusion, the presented object-tracking method holds immense potential for revolutionizing warehouse operations. Its applications include occupancy determination, safety monitoring, efficiency enhancement, and inventory management, contributing to the optimization and development of future advancements in human-centric logistics warehouse environments. Such a combination also provides value-added support by enabling data-driven decision-making to optimize resource allocation for enterprises.  
However, it is pertinent to acknowledge that the computational demands of the proposed method could increase significantly with a larger number of objects and types of employees. This might necessitate more distributed computing resources, which could impact the feasibility of real-time tracking. The proposed algorithm may need to be adapted to handle a larger volume of data while maintaining reasonable processing times. A careful balance between algorithmic sophistication, computational efficiency, and practical considerations may be required to address these inherent limitations and trade-offs.