Chen C.-YRUEY-BEEI WU2023-06-092023-06-09202221642958https://www.scopus.com/inward/record.uri?eid=2-s2.0-85126734537&doi=10.1109%2fRWS53089.2022.9719930&partnerID=40&md5=69b4cbe8c24daa5b465727a30011e719https://scholars.lib.ntu.edu.tw/handle/123456789/632370Wi-Fi fingerprint positioning has the advantages of being infrastructure-less and easily accessible, but the weaknesses in terms of lower accuracy and limited Wi-Fi scanning speed are also hard to tackle. On the other hand, with the progress in computer vision and deep learning, vision-based positioning based on commonly available surveillance cameras becomes a promising solution for providing location-based services. But the major difficulty lies in checking the identity of detected people just by the captured images. This paper proposed a novel Matching Mechanism to address the identity matching problem, which associates the non-identifiable positioning sources like vision to the easily identifiable positioning sources like smart phone's Wi-Fi. Practicalities like scalability and online operation are considered in both the design and implementation of the mechanism. As a result, the experiment not only proved the effectiveness of matching the vision-based and Wi-Fi positioning results but also showed an improvement in positioning accuracy by over 60%. © 2022 IEEE.Computer Vision; Internet of Things; Location-based service; Object-detection model; Wi-Fi fingerprintingComputer vision; Deep learning; Encoding (symbols); Internet of things; Network security; Object detection; Security systems; Smartphones; Telecommunication services; Wireless local area networks (WLAN); Fusion systems; Location-based services; Matching mechanisms; Matching problems; Object-detection model; Scanning speed; Smart phones; Surveillance cameras; Vision-based positioning; Wi-fi fingerprinting; Location based servicesconference paper10.1109/RWS53089.2022.97199302-s2.0-85126734537