EV-Loc: Integrating Electronic and Visual Signals for Accurate Localization

Abstract

Nowadays, an increasing number of objects can be represented by their wireless electronic identifiers. For example, people can be recognized by their phone numbers or their phones' WiFi MAC addresses and products can be identified by their RFID numbers. Localizing objects with electronic identifiers is increasingly important as our lives become increasingly "digitalized". However, traditional wireless localization techniques cannot meet the fast growing needs of accurate and cost efficient localization. Some of these techniques require expensive hardware to achieve high accuracy, which is impractical for massive deployment. Others, such as WiFi RSSI based localization, are inaccurate and not robust to environmental noise. In this paper, we propose a new localization technique called EV-Loc. In EV-Loc, we use visual signals to help improve the accuracy of wireless localization. Our technique fully leverages visual signals' high accuracy and electronic signals' pervasiveness. To effectively couple these two signals, we design an E-V match engine to find the correspondence between an object's electronic identifier and its visual appearance. We implement our technique on mobile devices and evaluate it in real-world scenarios. The localization error is less than 1 m. We also evaluate our approach using large scale simulations. The results show that our approach is accurate and robust.

Nowhere to Hide: Cross-modal Identity Leakage between Biometrics and Devices

“…Data association is widely used in radar systems, when tracking blips on a radar screen [10], as well as in object monitoring of surveillance systems [24]. When it comes to the cross-modal association, research attention is limited and all dedicated to location tracking of humans [6,49,59]. These methods heavily rely on the hypothesis that both sensor modalities are observing evolving state spaces matched precisely in the temporal domain.…”

A Review of Hybrid Indoor Positioning Systems Employing WLAN Fingerprinting and Image Processing

“…EV-Loc is another example of the same combination of techniques. Teng [69] proposed a localization technique called EV-Loc. They used a matching engine to find the correspondence between an electronic identifier (E) and its visual appearance (V).…”

“…Teng et al [69] • Matching engine to find the correspondence between an electronic identifier and its visual appearance. proved four-layer deep neural network that integrated a Convolutional Neural Network (CNN) and an improved particle filter.…”

Assisted Living System with Adaptive Sensor’s Contribution

“…The two subsystems have complementary properties i.e., the radiofrequency localizer solves the occlusions that may occur in the computer vision detector, and the computer vision subsystem increases the accuracy of positions measured with the radiofrequency localizer. This model falls in the larger category of bimodal position and activity sensing systems also developed by other authors for analysis of shoppers [ 43 , 44 ], pedestrians [ 45 , 46 ], or just human pose recognition [ 47 ]. Both subsystems are independent and separately process the RF and RGBD sensors produced data.…”