Research Article Open Access

Gender-Based Customer Counting System Using Computer Vision for Retail Stores

Intan Sari Areni1, Tiwi Nur Safitri1, Indrabayu1 and Anugrayani Bustamin1
  • 1 Hasanuddin University, Indonesia
Journal of Computer Science
Volume 16 No. 4, 2020, 439-451

DOI: https://doi.org/10.3844/jcssp.2020.439.451

Submitted On: 13 January 2020 Published On: 16 April 2020

How to Cite: Areni, I. S., Safitri, T. N., Indrabayu, . & Bustamin, A. (2020). Gender-Based Customer Counting System Using Computer Vision for Retail Stores. Journal of Computer Science, 16(4), 439-451. https://doi.org/10.3844/jcssp.2020.439.451

Abstract

The development of modern retail business is gradually getting faster, increasing the level of competition among retailers. The retailers are changing their business strategies to acquire new customers, maintain customer loyalty and improve customer service. One way to indicate the good market performance of a retail shop is to know the number and details of visitors based on gender. The number of visitors in retail shops can be observed by installing CCTV camera. In the existing shop system, CCTV used just for monitoring activities in the retail shop. Therefore, the data from the CCTV camera can be used to calculate the number of visitors based on gender automatically by utilizing computer vision. This study designed a system to count the number of female visitors through video data. The data acquisition process involved 89 people, 41 women and 48 men. The data are stored in .AVI video file format with a resolution of 720×1280 pixels. This system can be divided into three main stages, which are face detection using the Viola-Jones method, feature extraction using Gabor Filter 2D and classification using Support Vector Machine (SVM) method. The result of the study showed that the system can count the number of female and non-female visitors with an accuracy rate of 96.52%. The system performance will be improved by using another feature extraction and classification methods.

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Keywords

  • Computer Vision
  • Female Face Detection
  • Gabor Filter 2D
  • Support Vector Machine
  • Viola Jones