Using Object Detection and Data Analysis for Developing Customer Insights in a Retail Setting

Abstract

Object detection is gaining popularity in a spectrum of domains such as pose detection, self-driving cars, optical character recognition among others. However, it has scarcely been explored in retail settings. Integrating object detection with data science techniques, mainly quantitative statistics, can help derive valuable insights about consumer patterns. The main aim of this paper is to detect people from a video feed and store it in a database that can be queried using multiple aggregations to estimate several metrics. In the context of shops or malls, novel statistical measures such as footfall, conversion rate, and heat maps among others can be obtained. This will help in making data driven decisions based on historical data and patterns. Estimating the busy hours of the store can help the owners to optimize the staff allocation. The footfall trends can be used to evaluate the effectiveness of the marketing campaigns. In this paper, the proposed method is able to detect and count people from CCTV video feeds with an accuracy of 71.4% using a model suitable for devices with low computational power such as Raspberry Pi. The collected data is used to plot the footfall versus time graph.