Experiment Settings:As shown in Figure 10,we let multiple human subjects (4~8 people)stand or sit freely in the cafe, while wearing the RFID tagged badges.These"tagged"hu- man subjects are thus different in terms of heights,horizontal distance and vertical distance.Besides,they can be slightly 0 mer moving or turning with a limited speed or angle.It raises CS-Phase CS-RSSI CS-Phase more challenges than the free-space testing,since the human (a)Stationary situation (b)Slightly moving situation body may lead to many interferences like multi-path effect and energy absorption.We conducted experiments to evaluate the performance of match ratios,by varying the factors like the number of human subjects,the spacing between human subjects,and the moving state.We deploy our system in front of the human subjects with a distance of 1.5~3m.The default number of human subjects and the default average spacing is 26m0.9m people 5 people 8 people respectively 6 and 60 cm. (c)Different average spacing (d)Different number of people Performance Evaluation:Our solution can achieve fairly Figure 12.Evaluate the match ratios good matching accuracy to recognize multiple tagged human backgrounds.For the accuracy,most of the people have posi- subjects of different factors like the height,spacing,moving tive/very positive evaluation,since in most cases our solution state,etc.Figure 12(a)-(d)respectively shows the match ra- can achieve very good performance in accuracy.For the friend- tios with different configurations.Without loss of generality. liness of user interface,most of the people have positive/very we show the matching results of 5 randomly generated de- positive evaluation.due to the interesting vet simple design ployments with different spacing and heights of the human 40 subjects.In the first experiment,we let the human subjects remain stationary,i.e.,standing or sitting still,and evaluate the match ratios.As shown in Figure 12(a),our solution achieves a match ratio of 50%and 80%respectively with CS-RSSI and CS-Phase.In the second experiment,we let the human sub- jects keep in slightly moving state,i.e.,they may be moving or turning with a limited speed(<40cm/s)or angle (<30/s). Figure 13.Evaluation of the user experience:1)application meaning,2) As shown in Figure 12(b),our solution achieves a match ratio technical complexity,3)accuracy,4)friendliness of user interface. of 60%and 74%respectively with CS-RSSI and CS-Phase CONCLUSION AND FUTURE WORK In the third experiment,we vary the average spacing between In this paper,we design an RFID-based system to identify and the human subjects from 60cm to 90cm.As shown in Figure distinguish multiple RFID tagged objects in an augmented 12(c),our solution achieves an average match ratio of over reality system.We deploy additional RFID antennas to the 50%and 75%respectively with CS-RSSI and CS-Phase.In COTS depth camera,and propose a continuous scanning-based the fourth experiment,we vary the number of human subjects scheme to distinguish multiple tagged objects.The current from 4 to 8.As shown in Figure 12(d),our solution achieves implementation is a proof-of concept prototype for the"tell an average match ratio of over 45%and 70%respectively me what I see"vision.The size of the system is huge for wear- with CS-RSSI and CS-Phase.The performance reduction of able usages,and the battery usage is high for conventional CS-RSSI in the above experiments is mainly due to the energy applications.In the future design,we consider to miniatur- absorption of human bodies,which distracts the conventional ize the technical solution and integrate it into the wearable distribution of RSSI in RF-signals.Nevertheless.CS-Phase devices.For example,we can miniaturize the RFID antennas always achieves fairly good performance since the phase in and the 3D camera,and integrate them into the wearable hel- RF-signals is irrelevant to the energy absorption problems. mets/glasses for augmented reality applications.In this way, in order to perform the continuous scanning,the user only User Experience Evaluation:We invite a total of 44 people need to continuously turn her head from one side to the other (28 males and 16 females with different technical backgrounds. side with a certain angle.All the inherent information of the their ages range from 20 to 58)to use our system in the aug- detected objects can be shown on the screen of glasses mented reality applications,and evaluate their user experience Acknowledgments via the questionnaire surveys,including 1)application mean- ing,2)technical complexity,3)accuracy and 4)friendliness This work is supported in part by National Natural Science Foundation of China under Grant Nos.61472185,61373129 of user interface.Figure 13 shows the evaluation results.For 61321491,91218302,61502224:JiangSu Natural Science the application meaning,most of the people have positive/very Foundation,No.BK20151390:EU FP7 IRSES MobileCloud positive evaluation,they believe it is a promising approach Project under Grant No.612212;CCF-Tencent Open Fund for future augmented reality application.For the technical This work is partially supported by Collaborative Innovation complexity,several people have some negative evaluation, Center of Novel Software Technology and Industrialization. this is mainly because the current prototype system is fairly huge in size,and the RSSI/phase-based continuous scanning The work of Jie Wu was supported in part by NSF grants CNS 1449860,CNS1461932,CNS1460971,CNS1439672,CNS method may not be so intuitive for users with various technical 1301774.and ECCS1231461.Experiment Settings: As shown in Figure 10, we let multiple human subjects (4∼8 people) stand or sit freely in the cafe, while wearing the RFID tagged badges. These “tagged” hu￾man subjects are thus different in terms of heights, horizontal distance and vertical distance. Besides, they can be slightly moving or turning with a limited speed or angle. It raises more challenges than the free-space testing, since the human body may lead to many interferences like multi-path effect and energy absorption. We conducted experiments to evaluate the performance of match ratios, by varying the factors like the number of human subjects, the spacing between human subjects, and the moving state. We deploy our system in front of the human subjects with a distance of 1.5∼3m. The default number of human subjects and the default average spacing is respectively 6 and 60 cm. Performance Evaluation: Our solution can achieve fairly good matching accuracy to recognize multiple tagged human subjects of different factors like the height, spacing, moving state, etc. Figure 12(a)-(d) respectively shows the match ra￾tios with different configurations. Without loss of generality, we show the matching results of 5 randomly generated de￾ployments with different spacing and heights of the human subjects. In the first experiment, we let the human subjects remain stationary, i.e., standing or sitting still, and evaluate the match ratios. As shown in Figure 12(a), our solution achieves a match ratio of 50% and 80% respectively with CS-RSSI and CS-Phase. In the second experiment, we let the human sub￾jects keep in slightly moving state, i.e., they may be moving or turning with a limited speed (<40cm/s) or angle (<30◦ /s). As shown in Figure 12(b), our solution achieves a match ratio of 60% and 74% respectively with CS-RSSI and CS-Phase. In the third experiment, we vary the average spacing between the human subjects from 60cm to 90cm. As shown in Figure 12(c), our solution achieves an average match ratio of over 50% and 75% respectively with CS-RSSI and CS-Phase. In the fourth experiment, we vary the number of human subjects from 4 to 8. As shown in Figure 12(d), our solution achieves an average match ratio of over 45% and 70% respectively with CS-RSSI and CS-Phase. The performance reduction of CS-RSSI in the above experiments is mainly due to the energy absorption of human bodies, which distracts the conventional distribution of RSSI in RF-signals. Nevertheless, CS-Phase always achieves fairly good performance since the phase in RF-signals is irrelevant to the energy absorption problems. User Experience Evaluation: We invite a total of 44 people (28 males and 16 females with different technical backgrounds, their ages range from 20 to 58) to use our system in the aug￾mented reality applications, and evaluate their user experience via the questionnaire surveys, including 1) application mean￾ing, 2) technical complexity, 3) accuracy and 4) friendliness of user interface. Figure 13 shows the evaluation results. For the application meaning, most of the people have positive/very positive evaluation, they believe it is a promising approach for future augmented reality application. For the technical complexity, several people have some negative evaluation, this is mainly because the current prototype system is fairly huge in size, and the RSSI/phase-based continuous scanning method may not be so intuitive for users with various technical CS-RSSI CS-Phase The match ratio 0 0.2 0.4 0.6 0.8 1 Deployment 1 Deployment 2 Deployment 3 Deployment 4 Deployment 5 (a) Stationary situation CS-RSSI CS-Phase The match ratio 0 0.2 0.4 0.6 0.8 1 Deployment 1 Deployment 2 Deployment 3 Deployment 4 Deployment 5 (b) Slightly moving situation CS-RSSI CS-Phase The match ratio 0 0.5 1 0.6m 0.9m (c) Different average spacing CS-RSSI CS-Phase The match ratio 0 0.5 1 4 people 6 people 8 people (d) Different number of people Figure 12. Evaluate the match ratios backgrounds. For the accuracy, most of the people have posi￾tive/very positive evaluation, since in most cases our solution can achieve very good performance in accuracy. For the friend￾liness of user interface, most of the people have positive/very positive evaluation, due to the interesting yet simple design. 1 2 3 4 Number of users 0 10 20 30 40 Negative Neutral Positive Very Positive Figure 13. Evaluation of the user experience: 1) application meaning, 2) technical complexity, 3) accuracy, 4) friendliness of user interface. CONCLUSION AND FUTURE WORK In this paper, we design an RFID-based system to identify and distinguish multiple RFID tagged objects in an augmented reality system. We deploy additional RFID antennas to the COTS depth camera, and propose a continuous scanning-based scheme to distinguish multiple tagged objects. The current implementation is a proof-of concept prototype for the “tell me what I see” vision. The size of the system is huge for wear￾able usages, and the battery usage is high for conventional applications. In the future design, we consider to miniatur￾ize the technical solution and integrate it into the wearable devices. For example, we can miniaturize the RFID antennas and the 3D camera, and integrate them into the wearable hel￾mets/glasses for augmented reality applications. In this way, in order to perform the continuous scanning, the user only need to continuously turn her head from one side to the other side with a certain angle. All the inherent information of the detected objects can be shown on the screen of glasses. Acknowledgments This work is supported in part by National Natural Science Foundation of China under Grant Nos. 61472185, 61373129, 61321491, 91218302, 61502224; JiangSu Natural Science Foundation, No. BK20151390; EU FP7 IRSES MobileCloud Project under Grant No. 612212; CCF-Tencent Open Fund. This work is partially supported by Collaborative Innovation Center of Novel Software Technology and Industrialization. The work of Jie Wu was supported in part by NSF grants CNS 1449860, CNS 1461932, CNS 1460971, CNS 1439672, CNS 1301774, and ECCS 1231461