Advanced Customer Behavior Tracking and Heatmap Analysis with YOLOv5 and DeepSORT in Retail Environment

• DOI: 10.3390/electronics13234730
• Publication Date: 2024-11-29T13:24:14Z
• Authors: Mohamed Shili; Sudarsan Jayasingh; Salah Hammedi
• Keywords: Tracking (education); Retail industry

This paper presents a computer-vision-based approach designed to enhance product placement and sales strategies in physical retail stores through real-time analysis of customer behavior. Our method employs DeepSORT for tracking YOLOv5 object identification generate heatmaps that illustrate consumer movement patterns engagement levels across various locations. To precisely track paths, the procedure starts with collection video material, which is then analyzed. Customer interaction traffic zones are represented using heatmap visualization, offers useful information about preferences popularity. In order maximize optimize shopping experience, businesses may use findings this improve placements, store layouts, marketing strategies. With its low intervention requirements scalable non-intrusive solution, system be used variety environments. solution requires minimal intervention, making it adaptable different settings. demonstrate approach’s effectiveness identifying strategic areas improvement adapting environments based on data. study underscores potential computer vision analytics, enabling data-driven decisions both satisfaction operational efficiency. gives merchants data develop more responsive, customized, effective experiences by providing dynamic perspective Retailers promote modernized customer-centered management strategy creative application match tactics shop design real behaviors.