## 1 Introduction  
Recently, deep learning methods have been proven to provide the most effective performance in many problems. With rapid advancements in artificial intelligence technology, brick-and-mortar stores face substantial difficulty compared with the immense data-driven understanding accessible to online vendors [1, 2]. Although e-commerce platforms have access to an exhaustive assessment of customer preferences and behaviors, brick-and-mortar stores often need more machinery to gather and examine comparable real-time information [3]. The problem at hand is that traditional retail environments need help to efficiently monitor and understand customer behavior in real time, which obstructs their capability to enhance store operation, boost customer experiences, and maintain a competitive edge contrary to online retailers. Watching purchasing tendencies in physical stores is essential for the best possible store activity, improving customer experiences, and preserving a competitive edge [4]. Typically, physical stores count on circuitous processes like selling information examinations and handbook remarks to determine customer behavior. Their method frequently offers belated understanding and breaks down to identifying the active type of in-store interactions. To examine this restriction, there is an increasing necessity for forward technologies that can provide real-time analytics immediately from the retail environment [5; 6; 7]. Compared to e-commerce platforms, traditional stores face real-time obstacles in tracking customer behavior. Online merchants use a wealth of information about customer interactions to produce quick and data-driven modifications, including click patterns and browsing histories. However, physical businesses need the greater immediacy and specificity of e-commerce insights and instead rely primarily on manual observations and delayed sales data. A solution is provided by integrating real-time tracking technologies, such as DeepSORT for tracking and YOLOv5 for object identification, which provides immediate insight into high-traffic areas, product interactions, and in-store consumer movements. This approach is near a big gap between the physical and digital retail worlds by enabling conventional merchants to customize consumer experiences, enhance shop layouts, and change inventory based on real-time behavioral information. While many studies focus on digital retail environments (e-commerce), the application of AI-driven technologies in physical stores is still developing. This research helps bridge that gap by demonstrating how state-of-the-art object detection and tracking technologies can be adapted and evaluated in real-world retail. Incorporating object detection methods like YOLOv5 (You Only Look Once version 5) for surveillance system preference and DeepSORT (Deep Simple Online and Real-Time Tracking) constitutes considerable progress for physical stores in assessing consumer behavior [8]. However, e-commerce leverages big data to think about customer preferences and behaviors, and conventional stores frequently need help to capture and analyze real-time information [9; 10]. By using YOLOv5, which stands out in accurate object and person detection, combined with DeepSORT for successful tracking, vendors can profit from a precious understanding of customer displacement and product interactions. This optimizes store arrangement and enhances customer experience by delivering real-time assessments. This novel approach allows physical stores to contend with online platforms by rapidly adjusting their approach based on discovered shopping behaviors [11]. YOLOv5's forward object detection technology recognizes objects, people, and other essential components inside a shopping environment. Combined with the effective tracking system from DeepSORT, this arrangement offers real-time information on consumer movements, product interaction, and traffic hot points [11; 12].