## 2 Related Work
Moreover, different environmental conditions, including variations in illumination or weather, might affect the system's efficiency and thus impact tracking accuracy. In [23], the authors present practical research on using computer vision models to detect customer activity within Mexican retail stores, which is focused on improving customer experience and store effectiveness. To find the best model for real-time customer tracking, Lytica Company, in partnership with Steren shops, undertook a study that evaluated solutions, including SSD Mobilenet V2, YOLO-FastestV2, YOLOv5, and YOLOvn. Key issues in video analysis are discussed, such as hardware constraints, camera placement, fluctuating illumination, and consumer obstruction. When the models' F1-score performance was assessed, the YOLOv5 versions (YOLOv5n and YOLOv5s) had the best accuracy, at 0.86. However, YOLOvn has proven to be a viable substitute for low-power devices, offering a satisfactory balance between precision and processing speed (FPS). The research highlights how computer vision is utilized in retail data analysis and the factors that must be considered when selecting suitable models for in-store surveillance in diverse environments. Table 1 compares the proposed approach with related work on defying the recommendation system. The subjects each paper addresses in the table are denoted by the (\(\surd\)) symbol, while the unresolved points are denoted by (\(\times\)). The examined study explains various methods leveraging machine learning and computer vision to improve retail. For example, when used in e-commerce situations, Ref. [16] shows the excellent classification accuracy of integrated deep learning models such as LeNet and ResNet. Yet, it also indicates limits in adjusting to more dynamic retail scenarios. Similarly, Ref. [17] presents a YOLOv5-based architecture that, while potentially limited in its application to store forms, successfully lowers the digital load and enhances item recognition accuracy in unmanned retail settings. Security-focused work, like [18], emphasizes using YOLO with Raspberry Pi to distinguish between guests and possible intruders. This is a cost-effective solution, but it has limited scalability and depends on less expensive hardware. In contrast, Ref. [19] investigates consumer behavior by tracking engagement and product interactions in real time with RFID and machine learning. This approach provides valuable information and involves significant setup expenses for RFID devices. A zero-shot learning strategy for product detection is used in [20] to overcome the drawbacks of conventional template-based systems, increasing versatility at the expense of higher processing load. Real-time tracking experiments like [21; 23] further demonstrate the capabilities of DeepSORT and YOLOv5 in dynamic, high-speed settings. In [21], it utilizes YOLOv5s_DSC to track complex traffic, reduce computing effort, and achieve fast speed, but it requires many resources to scale. Computer vision models are effectively used in real-world retail settings in studies [22; 23], where they follow consumer movements and optimize shop layouts. However, these studies are limited by hardware restrictions, variable illumination, and customer occlusions. Significant research gaps still exist despite the progress achieved in item detection and customer monitoring in retail settings. The integration of these methods into a coherent real-time recommendation system needs to be addressed in most current research, which