Tracking In-Cabin Astronauts Using Deep Learning and Head Motion Clues

• Main Authors: Rui Zhang, Yulin Zhang, Xueyang Zhang
• Format: Article
• Language: English
• Published: IEEE 2021-01-01
• Series: IEEE Access
• Subjects: Person-following robot; astronaut assistance; space station; deep learning; Tegra X2
• Online Access: https://ieeexplore.ieee.org/document/9305234/

A person-following robot is under development for astronaut assistance on the Chinese Space Station. Real-time astronaut detection and tracking are the most important prerequisites for in-cabin flying assistant robots so that they can follow a specific astronaut and offer him/her assistance. In the limited space in the space station cabin, astronauts stand close to each other when working collaboratively; thus, large regions of their bodies tend to overlap in the image. In addition, because astronauts wear the same clothes most of the time, it is difficult to distinguish an individual astronaut using human body features. In this paper, we distinguish the astronauts by tracking their heads in the image. A deep learning model trained using big data is proposed for effective head detection. In addition, a motion model based on spatial clues is combined with the head detection results to track astronauts in the scene. A complete pipeline of the algorithm has been implemented and run efficiently on the Tegra X2 embedded AI microprocessor. A set of experiments were carried out and successfully validated the effectiveness of the proposed tracking algorithm. This algorithm is a step toward the implementation of robot assistants, especially in resource-limited environments.