With computer vision-based technologies, current Augmented reality (AR) systems can effectively recognize multiple objects with different visual characteristics. However, only limited degrees of distinctions can be offered among different objects with similar natural features, and inherent information about these objects cannot be effectively extracted. In this paper, we propose TaggedAR, i.e., an RFID-based approach to assist the recognition of multiple tagged objects in AR systems, by deploying additional RFID antennas to the COTS depth camera. By sufficiently exploring the correlations between the depth of field and the received RF-signal, we propose a rotate scanning-based scheme to distinguish multiple tagged objects in the stationary situation, and propose a continuous scanning-based scheme to distinguish multiple tagged human subjects in the mobile situation. By pairing the tags with the objects according to the correlations between the depth of field and RF-signals, we can accurately identify and distinguish multiple tagged objects to realize the vision of “tell me what I see” from the AR system. We have implemented a prototype system to evaluate the actual performance with case studies in a real-world environment. The experiment results show that our solution achieves an average match ratio of 91 percent in distinguishing up to dozens of tagged objects with a high deployment density.

Nowadays, people usually depend on augmented reality (AR) systems to obtain an augmented view in a real-world environment. With the help of advanced AR technology (e.g. object recognition), users can effectively distinguish multiple objects of different types. However, these techniques can only offer limited degrees of distinctions among different objects and cannot provide more inherent information about these objects. In this paper, we leverage RFID technology to further label different objects with RFID tags. We deploy additional RFID antennas to the COTS depth camera and propose a continuous scanning-based scheme to scan the objects, i.e., the system continuously rotates and samples the depth of field and RF-signals from these tagged objects. In this way, by pairing the tags with the objects according to the correlations between the depth of field and RF-signals, we can accurately identify and distinguish multiple tagged objects to realize the vision of "tell me what I see" from the augmented reality system. For example, in front of multiple unknown people wearing RFID tagged badges in public events, our system can identify these people and further show their inherent information from the RFID tags, such as their names, jobs, titles, etc. We have implemented a prototype system to evaluate the actual performance. The experiment results show that our solution achieves an average match ratio of 91% in distinguishing up to dozens of tagged objects with a high deployment density.

In this paper, we leverage RFID technology to label different objects with RFID tags, so as to realize the vision of "show me what I see from the augmented reality system". We deploy additional RFID antennas to the COTS depth camera and propose a continuous scanning-based scheme to scan the objects, i.e., the system continuously rotates and samples the depth of field and RF-signals from these tagged objects. In this way, we can accurately identify and distinguish multiple tagged objects, by pairing the tags with the objects according to the correlations between the depth of field and RF-signals. Our solution achieves an average match ratio of 91% in distinguishing up to dozens of tagged objects with a high deployment density.