Customer Detection and Tracking using Computer Vision, YOLO and Hardware Integration: A Retail Analytics Approach

Kamaru Adzha  Kadiran; Luqman Hakim  Hairurizal; Rozi  Rifin; Siti Musliha Ajmal  Mokhtar; Mohamad Zhafran  Hussin

doi:10.24191/jaeds.v6i1.176

Authors

Kamaru Adzha Kadiran Faculty of Electrical Engineering, Universiti Teknologi MARA, Cawangan Johor Kampus Pasir Gudang, Johor, Malaysia.
Luqman Hakim Hairurizal Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, 40450 Shah Alam, Selangor, Malaysia
Rozi Rifin Faculty of Electrical Engineering, Universiti Teknologi MARA, Cawangan Johor Kampus Pasir Gudang, Johor, Malaysia
Siti Musliha Ajmal Mokhtar Faculty of Electrical Engineering, Universiti Teknologi MARA, Cawangan Johor Kampus Pasir Gudang, Johor, Malaysia
Mohamad Zhafran Hussin Faculty of Electrical Engineering, Universiti Teknologi MARA, Cawangan Johor Kampus Pasir Gudang, Johor, Malaysia

DOI:

https://doi.org/10.24191/jaeds.v6i1.176

Keywords:

Computer Vision; Customer Tracking; Retail Analytics; Object Detection, OpenCV.

Abstract

This paper presents a comprehensive system for customer detection and tracking in retail environments using computer vision technology, YOLOv4 object detection, and hardware integration. The system combines software components including OpenCV, Python programming, and YOLOv4 neural networks with hardware elements such as Arduino UNO, LCD displays, and custom PCB boards to provide real-time customer analytics. The implementation focuses on tracking customer movement within specific regions of interest (ROI) to help retail store owners analyse customer attraction performance and optimize store layouts.

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