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.

翻译：受近期零售业将机器人部署至面向客户岗位所面临挑战的驱动，本研究引入了一项对实体商店中顾客活动的分析，作为迈向自主理解购物者意图的一步。我们提出了一种计算购物者“货架访问”的算法——捕捉其在商店内的浏览行为。货架访问从基于机器视觉的3D追踪和顶置摄像头获取的轨迹中提取。我们使用在不同商店收集、并由人工标注员标注的两组独立轨迹数据集（分别包含8138条和15129条轨迹），对货架访问算法进行了两次独立的校准。随后，使用校准过程中预留的轨迹（包括来自校准所用同一商店的轨迹和来自另一商店的轨迹）对校准后的模型进行评估。结果分析表明，当在不同于校准环境的新环境中进行评估时，该算法仍能识别顾客的浏览活动。接着，我们利用该模型分析大量轨迹上顾客的“浏览模式”及其与商店实际购买行为的关系。最后，我们探讨了货架浏览信息如何可用于零售规划以及人机交互场景领域。