Abstract:

Augmented Reality (AR) integrated with Deep Learning (DL) and Computer Vision (CV) is transforming the retail landscape by offering immersive and intelligent shopping experiences. These technologies collectively enable retailers to bridge the gap between physical and digital commerce, enhancing consumer engagement and decision-making. However, current AR-driven retail systems often suffer from limitations such as poor object recognition accuracy, lack of real-time adaptability, and limited personalization, which reduce the overall effectiveness and user satisfaction. To address these challenges, this paper proposes an innovative framework titled "VISAR" (Vision-Intelligent Smart Augmented Retail), which combines advanced DL models with real-time CV algorithms to deliver context-aware, personalized retail experiences. VISAR utilizes convolutional neural networks (CNNs) for robust object detection, generative models for realistic AR overlays, and reinforcement learning to adaptively recommend products based on user interaction and preferences. The proposed method is implemented within a smart retail environment where customers interact with AR displays that dynamically recognize products, suggest alternatives, and visualize them in real-time settings (e.g., home or body). VISAR also integrates customer behavior analytics and visual sentiment detection to enhance product recommendations and refine inventory strategies. Experimental results demonstrate that VISAR significantly improves object recognition precision, recommendation relevance, and customer engagement compared to traditional AR retail systems. The framework shows promise in reshaping the future of retail by providing a scalable, intelligent, and interactive platform that not only boosts customer satisfaction but also operational efficiency for retailers.