We present RetailOpt, a novel opt-in, easy-to-deploy system for tracking customer movements in indoor retail environments. The system utilizes information presently accessible to customers through smartphones and retail apps: motion data, store map, and purchase records. The approach eliminates the need for additional hardware installations/maintenance and ensures customers maintain full control of their data. Specifically, RetailOpt first employs inertial navigation to recover relative trajectories from smartphone motion data. The store map and purchase records are then cross-referenced to identify a list of visited shelves, providing anchors to localize the relative trajectories in a store through continuous and discrete optimization. We demonstrate the effectiveness of our system through systematic experiments in five diverse environments. The proposed system, if successful, would produce accurate customer movement data, essential for a broad range of retail applications, including customer behavior analysis and in-store navigation. The potential application could also extend to other domains such as entertainment and assistive technologies.

翻译：我们提出RetailOpt，一种新颖的可选式、易部署系统，用于追踪室内零售环境中顾客的移动轨迹。该系统利用顾客当前通过智能手机与零售应用可获取的信息：运动数据、商店地图及购买记录。该方法无需额外硬件安装/维护，并确保顾客对其数据拥有完全控制权。具体而言，RetailOpt首先采用惯性导航从智能手机运动数据中恢复相对轨迹。随后，通过交叉引用商店地图与购买记录识别顾客访问过的货架列表，并利用连续与离散优化方法将这些相对轨迹锚定至商店内的实际位置。我们通过在五个不同环境中进行系统实验来验证该系统的有效性。若该方案成功实施，将能够生成精确的顾客移动数据，这对于客户行为分析与店内导航等广泛零售应用至关重要。其潜在应用还可延伸至娱乐与辅助技术等其他领域。