Haofan Cai
ABSTRACT
Person identification and tracking (PIT) is an essential research topic in computer vision (CV). A CV-based system typically needs to identify, locate, and track persons appearing in its sight. In this work, we propose RFTrack, an RFID and CV fushion system that enables cameras in public areas (like surveillance cameras) to 'recognize' the physical-identity(ID) of persons in the fields of view and track the persons with specific IDs with no training efforts. By asking the users to perform a simple authentication, the system will be aware of the targets' IDs in its sensing range. Later through comparing the motion trajectories derived from both camera videos and RF signal, we can associate RFID-tagged human objects in videos with their physical IDs. A preliminary study conducted shows that RFTrack can actively identify and track the RFID-tagged target objects using commercial RFID devices and cameras, in complex indoor environments where various multipath reflectors exist.
- Haofan Cai, Ge Wang, Xiaofeng Shi, Junjie Xie, Minmei Wang, and Chen Qian. 2019. When tags readeach other: Enabling low-cost and convenient tag mutual identification. In 2019 IEEE 27th International Conference on Network Protocols (ICNP). IEEE, 1--11.Google Scholar
Cross Ref
- Hao-Shu Fang, Shuqin Xie, Yu-Wing Tai, and Cewu Lu. 2017. RMPE: Regional Multi-person Pose Estimation. In ICCV.Google Scholar
- Hyunwoo Yu, Jaemin Lim, Kiyeon Kim, and Suk-Bok Lee. 2018. Pinto: enabling video privacy for commodity IoT cameras. In Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security. 1089--1101.Google ScholarDigital Library
Index Terms
Enabling identity-aware tracking by vision-RFID fusion: poster abstract
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