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Although our ambition was to use natural features, we had to focus on a marker tracking camera system, as for now the processing of natural features is still too slow for this application.
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The system for robot-assisted image-guided TMS contains six main components (Fig. 1): A: TMS coil, B: robot, C: robot controller, D: personal computer, E: infrared tracking camera, and F: headband with reflective markers.
This type of camera system is an improvement over the tracking camera system.
Their head was restrained by a forehead and chin rest, and the pupil tracking camera focused.
It recorded motion with three reflective markers tracked by VICON Nexus V1.7.1 and a 9-camera VICON MX motion analysis system.
Two important functional modules for the human tracking over camera networks are addressed, including human tracking within a camera and human tracking across non-overlapping cameras.
We divide human tracking over camera networks into two inter-related modules: human tracking within a camera and human tracking across non-overlapping cameras.
Each marker was tracked continuously by video-cameras outside the box and Vtr calculated by Gauss's theorem [2].
Each marker was tracked by six video cameras, three in front of the subject and three behind.
Each marker was tracked using four video cameras, positioned about 2 m above the patient and inclined downward, and the three-dimensional position of each marker was reconstructed by stereo-photogrammetry at a sampling rate of 50 Hz.
Finally, we measured our complete tracking system: camera, inertia tracker and Kalman filter, using an industrial robot as controllable motion source and a marker at 3.2 m.
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