{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/vi-fi-associating-moving-subjects-across","title":"Vi-Fi: Associating Moving Subjects across Vision and Wireless Sensors","arxiv_id":null,"date":"2022-07-18","proceeding":"ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN) 2022 7","authors":["Hansi Liu","Abrar Alali","Mohamed Ibrahim","Bryan Bo Cao","Nicholas Meegan","Hongyu Li","Marco Gruteser","Shubham Jain","Kristin Dana","Ashwin Ashok","Bin Cheng","HongSheng Lu"],"abstract":"In this paper, we present Vi-Fi, a multi-modal system that leverages a user’s smartphone WiFi Fine Timing Measurements (FTM) and inertial measurement unit (IMU) sensor data to associate the user detected on a camera footage with their corresponding smartphone identifier (e.g. WiFi MAC address). Our approach uses a recurrent multi-modal deep neural network that exploits FTM and IMU measurements along with distance between user and camera (depth information) to learn affinity matrices. As a baseline method for comparison, we also present a traditional non deep learning approach that uses bipartite graph matching. To facilitate evaluation, we collected a multi-modal dataset that comprises camera videos with depth information (RGB-D), WiFi FTM and IMU measurements for multiple participants at diverse real-world settings. Using association accuracy as the key metric for evaluating the fidelity of 𝑉𝑖𝑠𝑢𝑎𝑙4 𝑉𝑖𝑠𝑢𝑎𝑙5 Figure 1: Motivation: Successfully associating vision-wireless Vi-Fi in associating human users on camera feed with their phone IDs, we show that Vi-Fi achieves between 81% (real-time) to 91% (offline) association accuracy.","url_abs":"https://ieeexplore.ieee.org/document/9826015","url_pdf":"https://www.winlab.rutgers.edu/~hansiiii/papers/ViFi_Paper___IPSN_2022__Camera_Ready_.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"vi-fi-associating-moving-subjects-across","repo_url":"https://github.com/vifi2021/Vi-Fi","is_official":0,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"graph-matching","task_name":"Graph Matching"},{"task_slug":"multimodal-association","task_name":"Multimodal Association"}],"methods":[{"method_slug":"non","method_name":"NON"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}