  Human motion capture is frequently used to study rehabilitation and clinical
problems, as well as to provide realistic animation for the entertainment
industry. IMU-based systems, as well as Marker-based motion tracking systems,
are the most popular methods to track movement due to their low cost of
implementation and lightweight. This paper proposes a quaternion-based Extended
Kalman filter approach to recover the human leg segments motions with a set of
IMU sensors data fused with camera-marker system data. In this paper, an
Extended Kalman Filter approach is developed to fuse the data of two IMUs and
one RGB camera for human leg motion tracking. Based on the complementary
properties of the inertial sensors and camera-marker system, in the introduced
new measurement model, the orientation data of the upper leg and the lower leg
is updated through three measurement equations. The positioning of the human
body is made possible by the tracked position of the pelvis joint by the camera
marker system. A mathematical model has been utilized to estimate joints' depth
in 2D images. The efficiency of the proposed algorithm is evaluated by an
optical motion tracker system.

Computer Vision and Pattern Recognition