Analyzing the Shopping Journey: Computing Shelf Browsing Visits in a Physical Retail Store
This work addresses the challenge of deploying robots in retail by providing a method to analyze customer behavior, though it appears incremental as it applies existing tracking methods to a new domain-specific problem.
The paper tackles the problem of understanding shopper intent in physical retail stores by developing an algorithm that computes shelf visits from customer trajectories obtained via machine vision. The algorithm was calibrated on two datasets (8138 and 15129 trajectories) and shown to recognize browsing activity across different store environments, with analysis linking browsing patterns to actual purchases.
Motivated by recent challenges in the deployment of robots into customer-facing roles within retail, this work introduces a study of customer activity in physical stores as a step toward autonomous understanding of shopper intent. We introduce an algorithm that computes shoppers' ``shelf visits'' -- capturing their browsing behavior in the store. Shelf visits are extracted from trajectories obtained via machine vision-based 3D tracking and overhead cameras. We perform two independent calibrations of the shelf visit algorithm, using distinct sets of trajectories (consisting of 8138 and 15129 trajectories), collected in different stores and labeled by human reviewers. The calibrated models are then evaluated on trajectories held out of the calibration process both from the same store on which calibration was performed and from the other store. An analysis of the results shows that the algorithm can recognize customers' browsing activity when evaluated in an environment different from the one on which calibration was performed. We then use the model to analyze the customers' ``browsing patterns'' on a large set of trajectories and their relation to actual purchases in the stores. Finally, we discuss how shelf browsing information could be used for retail planning and in the domain of human-robot interaction scenarios.