Summary of Retailopt: Opt-in, Easy-to-deploy Trajectory Estimation From Smartphone Motion Data and Retail Facility Information, by Ryo Yonetani and Jun Baba and Yasutaka Furukawa
RetailOpt: Opt-In, Easy-to-Deploy Trajectory Estimation from Smartphone Motion Data and Retail Facility Information
by Ryo Yonetani, Jun Baba, Yasutaka Furukawa
First submitted to arxiv on: 19 Apr 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Robotics (cs.RO)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The RetailOpt system uses existing smartphone and retail app data to track customer movements offline in indoor retail environments. By leveraging inertial navigation and cross-referencing store maps with purchase records, the system can accurately localize customer trajectories without requiring additional hardware or compromising customers’ privacy. This innovative approach has the potential to revolutionize retail applications such as customer behavior analysis and in-store navigation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The RetailOpt system is a new way to track where customers move in stores using information from their phones and store apps. It doesn’t need any extra equipment or maintenance, and lets customers keep control of their data. The system uses phone motion sensors to figure out where customers are moving relative to each other, then uses maps and purchase records to pinpoint exactly where they are in the store. This could be really helpful for stores that want to understand how customers behave and navigate their spaces. |
