Fusing Video and Inertial Sensor Data for Walking Person Identification
This addresses the need for efficient and practical person identification in autonomous systems like robots, though it is incremental as it builds on existing sensor fusion methods.
The paper tackles the problem of identifying walking persons by fusing video and inertial sensor data, achieving a correct identification rate of up to 76% within 2 seconds.
An autonomous computer system (such as a robot) typically needs to identify, locate, and track persons appearing in its sight. However, most solutions have their limitations regarding efficiency, practicability, or environmental constraints. In this paper, we propose an effective and practical system which combines video and inertial sensors for person identification (PID). Persons who do different activities are easy to identify. To show the robustness and potential of our system, we propose a walking person identification (WPID) method to identify persons walking at the same time. By comparing features derived from both video and inertial sensor data, we can associate sensors in smartphones with human objects in videos. Results show that the correctly identified rate of our WPID method can up to 76% in 2 seconds.