Authors: A. Hossam, A. Ramadan, M. Magdy, R. Abdelwahab, S. Ashraf, Z. Mohamed

Published on: February 24, 2024

Impact Score: 7.6

Arxiv code: Arxiv:2405.00023

Summary

• What is new: Introduction of a smart retail analytics system (SRAS) leveraging machine learning for enhanced retail efficiency and customer engagement. A novel hybrid architecture combining YOLOV8 with BOT-SORT and ByteTrack for customer tracking.
• Why this is important: Inefficient queue management, poor demand forecasting, and ineffective marketing in the retail sector.
• What the research proposes: An advanced SRAS utilizing a hybrid architecture for customer tracking and predictive models for inventory management.
• Results: Exceptional performance in customer tracking with YOLOV8 and superior inventory management predictions using the GRU model.

Technical Details

Technological frameworks used: nan

Models used: YOLOV8, BOT-SORT, ByteTrack, GRU, Linear Regression.

Data used: Surveillance footage from retail environments for YOLOV8, complex retail data patterns for predictive modeling.

Potential Impact

Retail sector, including brick-and-mortar stores, could benefit greatly; marketing companies focusing on retail analytics might face disruption.

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