RetailOpt: Opt-In, Easy-to-Deploy Trajectory Estimation from Smartphone Motion Data and Retail Facility Information
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866911956555792384 |
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| author | Yonetani, Ryo Baba, Jun Furukawa, Yasutaka |
| author_facet | Yonetani, Ryo Baba, Jun Furukawa, Yasutaka |
| contents | We present RetailOpt, a novel opt-in, easy-to-deploy system for tracking customer movements offline in indoor retail environments. The system uses readily accessible information from customer smartphones and retail apps, including motion data, store maps, and purchase records. This eliminates the need for additional hardware installations/maintenance and ensures customers full data control. Specifically, RetailOpt first uses inertial navigation to recover relative trajectories from smartphone motion data. The store map and purchase records are 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 in five diverse environments. The 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_12548 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | RetailOpt: Opt-In, Easy-to-Deploy Trajectory Estimation from Smartphone Motion Data and Retail Facility Information Yonetani, Ryo Baba, Jun Furukawa, Yasutaka Artificial Intelligence Robotics We present RetailOpt, a novel opt-in, easy-to-deploy system for tracking customer movements offline in indoor retail environments. The system uses readily accessible information from customer smartphones and retail apps, including motion data, store maps, and purchase records. This eliminates the need for additional hardware installations/maintenance and ensures customers full data control. Specifically, RetailOpt first uses inertial navigation to recover relative trajectories from smartphone motion data. The store map and purchase records are 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 in five diverse environments. The 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. |
| title | RetailOpt: Opt-In, Easy-to-Deploy Trajectory Estimation from Smartphone Motion Data and Retail Facility Information |
| topic | Artificial Intelligence Robotics |
| url | https://arxiv.org/abs/2404.12548 |