TY - GEN
T1 - RFCamera: Identifying RFIDs in Pixel Dimensions
AU - Lin, Qiongzheng
AU - Yang, Lei
AU - An, Zhenlin
AU - Guo, Yi
AU - Li, Ping
N1 - Funding Information:
these works focus on the robot navigation, while our task is to make robots understand their visual inputs. IX. ACKNOWLEDGMENT The research is supported by NSFC for Young Scientists of China (NO. 61902331), NSFC General Program (NO. 61972331), NSFC Key Program (NO. 61932017) and UGC/ECS (NO. 25222917).
Publisher Copyright:
© 2020 IEEE.
PY - 2020/6
Y1 - 2020/6
N2 - Radio Frequency IDentification (RFID) is emerging as a vital technology of the Internet of Things. Billions of RFID tags have been deployed to locate daily objects such as equipment, pharmaceuticals, and vehicles, and so on. Unlike previous solutions that focus on localizing tagged objects in the world coordinate system in reference to reader antennas, this work exploits a system, called RFCamera, that can identify and locate RFID-tagged objects in images with pixel dimensions. Many applications would benefit from RFCamera. For instance, the RF-aware image annotation system is able to generate rich annotations for RFID-tagged entities in images at the pixel level for the deep learning; the RF-aware auto-focus allows surveillance camera to exactly focalize the burglar who carries the stolen tagged-property out of a crowd. Our core insight is that an image is a visual AoA profile in terms of lights, which is resulted from the pinhole camera model. Similarly, we generate an RF image, derived from the AoA profile of a tag using the same pinhole model as the camera. Consequently, the locations of visual entities corresponding to tagged objects are highlighted by comparing two types of images. To this end, we customized a camera system equipped with a pair of rotatable reader antennas. Our experimental evaluation demonstrates that RFCamera enables a mean error of 5.7° and 2.9° at azimuth and elevation angle estimation. It can locate a visual entity with a mean error of 51 pixels (i.e., ≈ 1.3 cm at 96 dpi) in a 640 × 480 image.
AB - Radio Frequency IDentification (RFID) is emerging as a vital technology of the Internet of Things. Billions of RFID tags have been deployed to locate daily objects such as equipment, pharmaceuticals, and vehicles, and so on. Unlike previous solutions that focus on localizing tagged objects in the world coordinate system in reference to reader antennas, this work exploits a system, called RFCamera, that can identify and locate RFID-tagged objects in images with pixel dimensions. Many applications would benefit from RFCamera. For instance, the RF-aware image annotation system is able to generate rich annotations for RFID-tagged entities in images at the pixel level for the deep learning; the RF-aware auto-focus allows surveillance camera to exactly focalize the burglar who carries the stolen tagged-property out of a crowd. Our core insight is that an image is a visual AoA profile in terms of lights, which is resulted from the pinhole camera model. Similarly, we generate an RF image, derived from the AoA profile of a tag using the same pinhole model as the camera. Consequently, the locations of visual entities corresponding to tagged objects are highlighted by comparing two types of images. To this end, we customized a camera system equipped with a pair of rotatable reader antennas. Our experimental evaluation demonstrates that RFCamera enables a mean error of 5.7° and 2.9° at azimuth and elevation angle estimation. It can locate a visual entity with a mean error of 51 pixels (i.e., ≈ 1.3 cm at 96 dpi) in a 640 × 480 image.
UR - http://www.scopus.com/inward/record.url?scp=85091985867&partnerID=8YFLogxK
U2 - 10.1109/SECON48991.2020.9158430
DO - 10.1109/SECON48991.2020.9158430
M3 - Conference article published in proceeding or book
AN - SCOPUS:85091985867
T3 - Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks workshops
SP - 1
EP - 9
BT - 2020 17th IEEE International Conference on Sensing, Communication and Networking, SECON 2020
PB - IEEE Computer Society
T2 - 17th IEEE International Conference on Sensing, Communication and Networking, SECON 2020
Y2 - 22 June 2020 through 25 June 2020
ER -