Interest in Radio Frequency (RF) based 3D human pose tracking has skyrocketed in the age of Artificial Intelligence of Things (AIoT). Compared to Computer Vision (CV) based methods, RF-based approaches are more resilient to lighting and non-line-of-sight conditions, and can better preserve user privacy. However, the majority of the current RF-based methods rely on a vision-aided multi-modal learning approach. An extensive amount of paired training data, i.e., Radio-Frequency Identification (RFID) data and vision data, must be collected, to achieve an adequate performance with the supervised-learning network. In order to mitigate such time-consuming and costly tasks, we propose a data augmentation method based on Generative Adversarial Network (GAN), named RFPose-GAN, to generate synthesized RFID data to alleviate the complications of using commodity RFID tags and receivers. In this paper, a forward kinematic layer is incorporated to generate simulated vision pose data, thus eliminating the need of using a Kinect 2.0 device in RFPose-GAN. Experiments conducted demonstrate that the synthesized data achieves accurate pose estimation performance.
Data Augmentation for RFID-based 3D Human Pose Tracking
2022-09-01
883402 byte
Conference paper
Electronic Resource
English
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