How Computer Vision Provides Physical Retail with a Better View on Customers

Abstract

In recent years, web-based retailers have been taking over a growing market share from traditional brick and mortar retailers. One of the advantages leveraged by online retail is its ability to personalize the customer journey by analyzing the massive amounts of data that can be acquired easily in a digital environment. For example, click-streams from a web shop can help to identify a customer's interests in order to generate individual recommendations. To keep up, physical retailers, too, have to transform into the digital world. The main requirement for this are suitable data acquisition methods as well as resulting applications that are viable in an offline setting. In this paper, we investigate how recent technologies from the field of Computer Vision can overcome the data acquisition bottleneck and allow for data-based innovations that help traditional retailers to improve their customers' shopping experience and consequently to strengthen their market position. To this end, we introduce a conceptual tracking system for offline retail stores. Its purpose is to generate movement tracks over time for individual customers, from the time the supermarket is entered until it is left again. The acquired data allow for several data-based applications that can achieve similar goals as their counterparts in online retail.

An In-store Recommender System Leveraging the Microsoft HoloLens

“…An alternative approach to obtain similar real-time information is computer vision [22] which however has some limitations. For example, to infer which product a customer is seeing using standard surveillance cameras would require a large amount of cameras placed in different locations, powerful hardware and algorithms to analyze the video streams.…”

“…Recently, [24] identified that resolving the complexity of the context data and creating more personalized advertisements as two of the directions where researchers need to contribute in order to improve mobile recommender systems and in the present article, we aim to work towards those directions. Another technology that has been integrated with recommender systems is computer vision which can be used to track customers in-store using surveillance cameras [22]. Other options include smart mirrors which detect RFID tags and recommend similar products [11].…”

A Hierarchy-Based System for Recognizing Customer Activity in Retail Environments

“…Most CAR researches focus on the human detection. Different sensors are applied to detect humans [ 2 ], in particular, WiFi RSS [ 5 , 6 , 7 ], RFID [ 11 ], GPS [ 12 ], Bluetooth beacons [ 8 , 9 , 10 ], RGB camera [ 3 , 13 , 14 , 20 , 21 , 26 ], RGB-depth camera [ 4 , 15 , 16 , 17 , 18 , 19 ]. Due to its descriptive nature, visual data are the preferred input for human detection.…”

“…Customer activity (CA) in retail environments, which ranges over various shopper situations in store spaces, provides valuable information for store management (e.g., layout optimization and shoplifting prevention) and marketing planning (e.g., supply control and product development) [ 1 , 2 ]. However, in traditional retail environments, available information tends to be limited to purchase records of customers, which cannot reveal any details of CA such as movement of customers in store spaces and interaction of customers with products [ 3 ].…”

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

“…Computer vision can also be used to generate movement tracks over time for individual customers, from the time the customer enter the supermarket until the customer left. The acquired data allowed for several data-based applications that can achieve similar goals as their counterparts in online retail (Hernandez et al, 2019). An example of research that implemented computer vision in retail activities is a system that can detect certain face among crowd.…”