Shopper Analytics: a customer activity recognition system using a distributed RGB-D camera network
This work addresses the need for automated customer analytics in retail to understand product interactions, though it is incremental as it builds on existing RGB-D techniques for activity recognition.
The paper tackles the problem of monitoring shopper behavior in retail environments by developing a low-cost RGB-D camera system that detects and identifies people and their interactions with products on shelves, such as picking up or returning items, with experimental results showing satisfactory performance in real settings.
The aim of this paper is to present an integrated system consisted of a RGB-D camera and a software able to monitor shoppers in intelligent retail environments. We propose an innovative low cost smart system that can understand the shoppers' behavior and, in particular, their interactions with the products in the shelves, with the aim to develop an automatic RGB-D technique for video analysis. The system of cameras detects the presence of people and univocally identifies them. Through the depth frames, the system detects the interactions of the shoppers with the products on the shelf and determines if a product is picked up or if the product is taken and then put back and finally, if there is not contact with the products. The system is low cost and easy to install, and experimental results demonstrated that its performances are satisfactory also in real environments.