Papers
Topics
Authors
Recent
Search
2000 character limit reached

RetailOpt: Opt-In, Easy-to-Deploy Trajectory Estimation from Smartphone Motion Data and Retail Facility Information

Published 19 Apr 2024 in cs.AI and cs.RO | (2404.12548v2)

Abstract: 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.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (93)
  1. Apurva Bedagkar-Gala and Shishir K Shah. 2014. A survey of approaches and trends in person re-identification. Image and Vision Computing 32, 4 (2014), 270–286.
  2. Trajectory outlier detection: Algorithms, taxonomies, evaluation, and open challenges. ACM Transactions on Management Information Systems (TMIS) 11, 3 (2020), 1–29.
  3. Paul J Besl and Neil D McKay. 1992. Method for registration of 3-D shapes. In Sensor Fusion IV: Control Paradigms and Data Structures, Vol. 1611. Spie, 586–606.
  4. Codeslam—learning a compact, optimisable representation for dense visual slam. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2560–2568.
  5. JAX: composable transformations of Python+NumPy programs. http://github.com/google/jax
  6. Computer vision and deep learning techniques for pedestrian detection and tracking: A survey. Neurocomputing 300 (2018), 17–33.
  7. RIO: Rotation-equivariance supervised learning of robust inertial odometry. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 6614–6623.
  8. STORE VIEW: pervasive RFID & indoor navigation based retail inventory management. In Proceedings of the ACM conference on Pervasive and Ubiquitous Computing Adjunct Publication (UbiComp Adjunct). 1037–1042.
  9. P Chandrasekar and T Sangeetha. 2014. Smart shopping cart with automatic billing system through RFID and ZigBee. In International Conference on Information Communication and Embedded Systems (ICICES2014). IEEE, 1–4.
  10. Motiontransformer: Transferring neural inertial tracking between domains. In Proceedngs of the AAAI Conference on Artificial Intelligence (AAAI), Vol. 33. 8009–8016.
  11. Wireless indoor localization using passive RFID tags. Procedia Computer Science 155 (2019), 210–217.
  12. SmartLOC: Indoor Localization with Smartphone Anchors for On-Demand Delivery. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT) 5, 4 (2022).
  13. Deep learning for visual SLAM in transportation robotics: A review. Transportation Safety and Environment 1, 3 (2019), 177–184.
  14. Ramsey Faragher and Robert Harle. 2015. Location fingerprinting with bluetooth low energy beacons. IEEE Journal on Selected Areas in Communications 33, 11 (2015), 2418–2428.
  15. Privacy vs. reward in indoor location-based services. Proceedings on Privacy Enhancing Technologies (2016).
  16. Pedestrian detection and tracking in video surveillance system: issues, comprehensive review, and challenges. Recent Trends in Computational Intelligence (2020), 1–24.
  17. Mobile targeting using customer trajectory patterns. Management Science 65, 11 (2019), 5027–5049.
  18. Extended coherent point drift algorithm with correspondence priors and optimal subsampling. In Proceedings of the IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE, 1–9.
  19. RFID light bulb: Enabling ubiquitous deployment of interactive RFID systems. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT) 1, 2 (2017), 1–16.
  20. Mahdi Hashemi and Hassan A Karimi. 2014. A critical review of real-time map-matching algorithms: Current issues and future directions. Computers, Environment and Urban Systems 48 (2014), 153–165.
  21. Neural inertial localization. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE.
  22. Fusion-dhl: Wifi, imu, and floorplan fusion for dense history of locations in indoor environments. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA). IEEE, 5677–5683.
  23. Osamu Hirose. 2020. A Bayesian formulation of coherent point drift. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 43, 7 (2020), 2269–2286.
  24. A comparative survey of WLAN location fingerprinting methods. In Proceedings of the IEEE Workshop on Positioning, Navigation and Communication. IEEE, 243–251.
  25. Xinyu Hou and Jeroen Bergmann. 2020. Pedestrian dead reckoning with wearable sensors: A systematic review. IEEE Sensors Journal 21, 1 (2020), 143–152.
  26. A survey on visual surveillance of object motion and behaviors. IEEE Transactions on Systems, Man, and Cybernetics, Part C 34, 3 (2004), 334–352.
  27. Survey on vehicle map matching techniques. CAAI Transactions on Intelligence Technology 6, 1 (2021), 55–71.
  28. The blocker tag: Selective blocking of RFID tags for consumer privacy. In Proceedings of the ACM conference on Computer and Communications Security (CCS). 103–111.
  29. Kamol Kaemarungsi and Prashant Krishnamurthy. 2004. Modeling of indoor positioning systems based on location fingerprinting. In Proceedings of the IEEE International Conference on Computer Communications (INFOCOM), Vol. 2. IEEE, 1012–1022.
  30. Effectiveness of cooperative customer navigation from robots around a retail shop. In Proceedings of the IEEE International Conference on Privacy, Security, Risk and Trust and International Conference on Social Computing. IEEE, 235–241.
  31. In-store mobile usage: Downloading and usage intention toward mobile location-based retail apps. Computers in Human Behavior 46 (2015), 210–217.
  32. Differentiable slam-net: Learning particle slam for visual navigation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2815–2825.
  33. probreg. https://probreg.readthedocs.io/en/latest/
  34. Eunhye Kim. 2021. In-store shopping with location-based retail apps: perceived value, consumer response, and the moderating effect of flow. Information Technology and Management 22, 2 (2021), 83–97.
  35. Kwan-Soo Kim and Yoan Shin. 2021. Deep Learning-Based PDR Scheme That Fuses Smartphone Sensors and GPS Location Changes. IEEE Access 9 (2021), 158616–158631.
  36. Diederik P Kingma and Jimmy Ba. 2015. Adam: A method for stochastic optimization. In Proceedings of the International Conference on Learning and Representation (ICLR).
  37. Towards a Better Understanding of Museum Visitors’ Behavior through Indoor Trajectory Analysis. In Proceedings of the International Conference on Digital Presentation and Preservation of Cultural and Scientific Heritage (DiPP), Vol. 7. 19–30.
  38. Panos Kourouthanassis and George Roussos. 2003. Developing consumer-friendly pervasive retail systems. IEEE Pervasive Computing 2, 02 (2003), 32–39.
  39. Mathieu Labbé and François Michaud. 2019. RTAB-Map as an open-source lidar and visual simultaneous localization and mapping library for large-scale and long-term online operation. Journal of Field Robotics 36, 2 (2019), 416–446.
  40. IoT applications on secure smart shopping system. IEEE Internet of Things Journal 4, 6 (2017), 1945–1954.
  41. A Bluetooth/PDR integration algorithm for an indoor positioning system. Sensors 15, 10 (2015), 24862–24885.
  42. Using Wi-Fi/magnetometers for indoor location and personal navigation. In Proceedings of the International Conference on Indoor Positioning and Indoor Navigation (IPIN). IEEE, 1–7.
  43. Image based localization in indoor environments. In Proceedings of the IEEE International Conference on Computing for Geospatial Research and Application. IEEE, 70–75.
  44. Tlio: Tight learned inertial odometry. IEEE Robotics and Automation Letters (RA-L) 5, 4 (2020), 5653–5660.
  45. TAR: Enabling fine-grained targeted advertising in retail stores. In Proceedings of the ACM Annual International Conference on Mobile Systems, Applications, and Services (MobiSys). Association for Computing Machinery, 323–336.
  46. Map-matching for low-sampling-rate GPS trajectories. In Proceedings of the ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. ACM New York, NY, USA, 352–361.
  47. Ultra-wideband Automatic Anchor’s Localization for Indoor Path Tracking. In Proceedings of the IEEE Sensors. IEEE, 1–4.
  48. The customer retail app experience: Implications for customer loyalty. Journal of Retailing and Consumer Services 65 (2022), 102842.
  49. Learning regularity in skeleton trajectories for anomaly detection in videos. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 11996–12004.
  50. Andriy Myronenko and Xubo Song. 2010. Point set registration: Coherent point drift. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 32, 12 (2010), 2262–2275.
  51. Robust visual localization across seasons. IEEE Transactions on Robotics 34, 2 (2018), 289–302.
  52. A survey of machine learning for indoor positioning. IEEE Access 8 (2020), 214945–214965.
  53. Variability in Reactions to Instructional Guidance during Smartphone-Based Assisted Navigation of Blind Users. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT) 2, 3, Article 131 (2018), 25 pages.
  54. Modelling and forecasting customer navigation in intelligent retail environments. Journal of Intelligent & Robotic Systems 91 (2018), 165–180.
  55. A survey on visual-based localization: On the benefit of heterogeneous data. Pattern Recognition 74 (2018), 90–109.
  56. SugarTrail: Indoor navigation in retail environments without surveys and maps. In Proceedings of the IEEE International Conference on Sensing, Communications and Networking (SECON). IEEE, 300–308.
  57. Current map-matching algorithms for transport applications: State-of-the art and future research directions. Transportation Research Part c: Emerging Technologies 15, 5 (2007), 312–328.
  58. Retail technologies that enhance the customer experience: a practitioner-centred approach. Humanities and Social Sciences Communications 10, 1 (2023), 1–8.
  59. Self-calibration and collaborative localization for UWB positioning systems: A survey and future research directions. ACM Computing Surveys (CSUR) 54, 4 (2021), 1–27.
  60. Multi-timescale trajectory prediction for abnormal human activity detection. In Proceedings of the IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE, 2626–2634.
  61. TinyOdom: Hardware-aware efficient neural inertial navigation. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT) 6, 2, Article 71 (2022).
  62. Visual SLAM and structure from motion in dynamic environments: A survey. ACM Computing Surveys (CSUR) 51, 2 (2018), 1–36.
  63. Fast image-based localization using direct 2d-to-3d matching. In Proceeding of the International Conference on Computer Vision (ICCV). IEEE, 667–674.
  64. Acceptance of in-store apps: Factors that influence the intention to adopt location-based retail apps–Insights from Germany. International Journal of Retail & Distribution Management 50, 6 (2022), 760–777.
  65. Mobile Location-Based Services’ Value-in-Use in Inner Cities: Do a Customer’s Shopping Patterns, Prior User Experience, and Sales Promotions Matter? Schmalenbach Business Review 72, 4 (2020), 511–564.
  66. Generalized-icp.. In Robotics: Science and Systems, Vol. 2. Seattle, WA, 435.
  67. IoT-based smart shopping cart using radio frequency identification. IEEE Access 8 (2020), 68426–68438.
  68. Map-matching algorithms for robot self-localization: a comparison between perfect match, iterative closest point and normal distributions transform. Journal of Intelligent & Robotic Systems 93 (2019), 533–546.
  69. Masato Sugasaki and Masamichi Shimosaka. 2017. Robust indoor localization across smartphone models with ellipsoid features from multiple rssis. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT) 1, 3 (2017), 1–16.
  70. Idol: Inertial deep orientation-estimation and localization. In Proceedngs of the AAAI Conference on Artificial Intelligence (AAAI). AAAI Press.
  71. Visual SLAM algorithms: A survey from 2010 to 2016. IPSJ Transactions on Computer Vision and Applications (CVA) 9, 1 (2017), 1–11.
  72. Registration of 3D point clouds and meshes: A survey from rigid to nonrigid. IEEE Transactions on Visualization and Computer Graphics 19, 7 (2012), 1199–1217.
  73. Indoor localization without infrastructure using the acoustic background spectrum. In Proceedings of the International Conference on Mobile Systems, Applications, and Services (MobiSys). 155–168.
  74. Zachary Teed and Jia Deng. 2021. Droid-slam: Deep visual slam for monocular, stereo, and rgb-d cameras. Proceedings of the Annual Conference on Neural Information Processing Systems (NeurIPS) 34 (2021), 16558–16569.
  75. Long-term visual localization revisited. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 44, 4 (2020), 2074–2088.
  76. Maria Valera and Sergio A Velastin. 2005. Intelligent distributed surveillance systems: A review. IEE Proceedings-Vision, Image and Signal Processing 152, 2 (2005), 192–204.
  77. Engaging the unengaged customer: The value of a retailer mobile app. International Journal of Research in Marketing 36, 3 (2019), 420–438.
  78. Attention is all you need. In Proceedings of the Annual Conference on Neural Information Processing Systems (NeurIPS).
  79. Raghav H. Venkatnarayan and Muhammad Shahzad. 2019. Enhancing Indoor Inertial Odometry with WiFi. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT) 3, 2, Article 47 (2019), 27 pages.
  80. Quoc Duy Vo and Pradipta De. 2015. A survey of fingerprint-based outdoor localization. IEEE Communications Surveys & Tutorials 18, 1 (2015), 491–506.
  81. Image-based localization using lstms for structured feature correlation. In Proceeding of the International Conference on Computer Vision (ICCV). IEEE, 627–637.
  82. No need to war-drive: Unsupervised indoor localization. In Proceedings of the International Conference on Mobile Systems, Applications, and Services (MobiSys). 197–210.
  83. DSP-SLAM: Object oriented SLAM with deep shape priors. In Proceedings of the IEEE International Conference on 3D Vision (3DV). IEEE, 1362–1371.
  84. Yue Wang and Justin M Solomon. 2019. Deep closest point: Learning representations for point cloud registration. In Proceeding of the International Conference on Computer Vision (ICCV). IEEE, 3523–3532.
  85. Mobile apps in retail: determinants of consumer acceptance–a systematic review. (2020).
  86. A survey of the research status of pedestrian dead reckoning systems based on inertial sensors. International Journal of Automation and Computing 16 (2019), 65–83.
  87. MaLoc: A practical magnetic fingerprinting approach to indoor localization using smartphones. In Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp). ACM New York, NY, USA, 243–253.
  88. The personalization privacy paradox: An exploratory study of decision making process for location-aware marketing. Decision support systems 51, 1 (2011), 42–52.
  89. RoNIN: Robust neural inertial navigation in the wild: Benchmark, evaluations, & new methods. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA). IEEE.
  90. RIDI: Robust IMU double integration. In Proceedings of the European Conference on Computer Vision (ECCV). Springer, 621–636.
  91. A survey of indoor localization systems and technologies. IEEE Communications Surveys & Tutorials 21, 3 (2019), 2568–2599.
  92. NeRF2: Neural radio-frequency radiance fields. In Proceedings of the ACM Annual International Conference on Mobile Computing and Networking (MobiCom). ACM New York, NY, USA, New York, NY, USA, Article 27.
  93. Heng Zhou and Takuya Maekawa. 2023. GPS-assisted Indoor Pedestrian Dead Reckoning. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT) 6, 4 (2023), 1–36.

Summary

No one has generated a summary of this paper yet.

Sign Up to Summarize

Paper to Video (Beta)

No one has generated a video about this paper yet.

Sign Up to Generate All Videos Subscribe on YouTube

Whiteboard

No one has generated a whiteboard explanation for this paper yet.

Sign Up to Generate

Open Problems

We haven't generated a list of open problems mentioned in this paper yet.

Generate Now

Continue Learning

We haven't generated follow-up questions for this paper yet.

Generate Now

Collections

Sign up for free to add this paper to one or more collections.

Sign Up

Tweets

Sign up for free to view the 1 tweet with 46 likes about this paper.

Sign Up for Free