Online People Tracking and Identification with RFID and Kinect
This system addresses indoor people tracking and identification for applications requiring privacy and lighting robustness, but it is incremental as it builds on existing sensor fusion methods.
The paper tackles real-time people tracking and identification by combining Kinect skeleton tracking with RFID tag matching, achieving centimeter-level tracking resolution and 80% average identification accuracy for up to six people in indoor environments.
We introduce a novel, accurate and practical system for real-time people tracking and identification. We used a Kinect V2 sensor for tracking that generates a body skeleton for up to six people in the view. We perform identification using both Kinect and passive RFID, by first measuring the velocity vector of person's skeleton and of their RFID tag using the position of the RFID reader antennas as reference points and then finding the best match between skeletons and tags. We introduce a method for synchronizing Kinect data, which is captured regularly, with irregular or missing RFID data readouts. Our experiments show centimeter-level people tracking resolution with 80% average identification accuracy for up to six people in indoor environments, which meets the needs of many applications. Our system can preserve user privacy and work with different lighting.