通过视觉追踪计算顾客在货架前的浏览行为，助力零售智能与人机交互。
Analyzing the Shopping Journey: Computing Shelf Browsing Visits in a Physical Retail Store
- 基于3D视觉追踪与顶视摄像头，提取顾客轨迹并识别货架浏览行为。
- 模型在跨店测试中仍能准确识别浏览活动，准确率超90%。
- 可分析顾客浏览模式与实际购买关系，适用于零售规划与机器人导购。
为应对机器人在零售场景中部署的挑战，本文研究实体店内顾客行为，以实现对购物意图的自主理解。提出一种算法，通过机器视觉驱动的3D追踪与天花板摄像头获取的轨迹数据，计算顾客的「货架访问」（shelf visits），捕捉其浏览行为。采用两组独立轨迹（分别包含8138条和15129条）进行校准，来自不同门店并由人工标注。校准后的模型在保留轨迹上评估，既在原门店也跨店测试，结果表明模型在不同环境下均能有效识别浏览行为。进一步利用该模型分析大规模轨迹数据中的浏览模式及其与实际购买的关系。最后讨论了货架浏览信息在零售规划及人-机器人交互场景中的应用潜力。
原文摘要 · Abstract (English)
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.
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