# Smart Logistics Warehouse Moving-Object Tracking Based on YOLOv5 and DeepSORT
###### Abstract  
The future development of Industry 4.0 places paramount importance on human-centered/-centric factors in the production, design, and management of logistic systems, which has led to the emergence of Industry 5.0. However, effectively integrating human-centered/-centric factors in logistics scenarios has become a challenge. A pivotal technological solution for dealing with such a challenge is to distinguish and track moving objects such as humans and goods. Therefore, an algorithm model combining YOLOv5 and DeepSORT for logistics warehouse object tracking is designed, where YOLOv5 is selected as the object-detection algorithm and DeepSORT distinguishes humans from goods and environments. The evaluation metrics from the MOT Challenge affirm the algorithm's robustness and efficacy. Through rigorous experimental tests, the combined algorithm demonstrates rapid convergence (within 30 ms), which holds promising potential for applications in real-world logistics warehouses.  
deep learning; YOLOv5; DeepSORT; logistics warehouse +
Footnote †: journal: Applied  
1
Footnote †: journal: Applied  
1
Footnote 1: email: _Applied_  
Footnote †: journal: Applied  
changes in the environment and the tracked entities themselves. Given the substantial volume of goods and individuals requiring tracking within logistics warehouses, some of the targets may move randomly, making it difficult for traditional machine vision algorithms to identify targets accurately. The newly developed object-recognition algorithms, grounded in deep learning, are able to automatically learn and extract features. Moreover, they can be adapted to complex scenarios, rendering them particularly well-suited for intelligent monitoring applications in logistics warehouses [6; 7].  
This study aims to provide an effective object-tracking approach within smart logistics warehouses. Additionally, it strives to formulate an object-tracking system tailored to logistics scenarios. The former provides auxiliary means for monitoring the operation status of logistics warehouses, ensuring smooth functioning and safety management. This approach aligns with the notion of the human-centric logistics production management concept. The latter serves as an illustrative model for the management method of sustainable and flexible industrial logistics systems. It also offers a pertinent method of object tracking to facilitate human-machine interaction in virtual reality. In sum, this study resonates with the theme of \"Sustainable, Human-Centered/Centric and Resilient Production and Logistic Systems Design and Management\".  
The specific work is as follows.  
1. Three prominent deep learning detection algorithms--Faster R-CNN, SSD, and YOLO--are briefly introduced and compared. The YOLO algorithm is chosen to analyze the logistics warehouse moving-object-tracking problem.
2. A dedicated dataset was meticulously crafted based on the logistics warehouse scenarios. The quality and quantity of the dataset were optimized, yielding enhanced outcomes in object detection specific to logistics warehouses.
3. The principle and process of DeepSORT algorithms are introduced. Finally, an evaluation metric for the effectiveness of logistics warehouse object tracking is established based on the MOT Challenge multi-object-tracking competition. This metric facilitates quantitative assessment, ultimately offering supplementary tools for diminishing the necessity of manual video surveillance within logistics settings.