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The other family of human motion capture algorithms is based on learning and classification instead of tracking.
As an example, a very highly accurate visualization of a human action needs motion capture algorithms that extract the location and angles of the various joints and limbs of the body.
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In this paper, we present a lossless compression algorithm for motion capture data.
The latter could be advantageously coupled with positioning algorithms for simultaneous body motion capture.
"Motion capture is good at large motions," he said.
Such a solution would require advance in both processing algorithms (such as single view motion capture) and in rendering techniques for fast visualization.
In this paper, a low-cost real-time multicamera algorithm for marker-based human motion capture is presented.
Two different control algorithms were studied using dynamic simulation and motion capture experiments with healthy subjects.
We examined and compared two different control algorithms for the robot using dynamic simulation, motion capture experiments in a controlled environment and most importantly experiments involving H.O.T. patients.
We describe the two leader following control algorithms and evaluate them using computer simulation and motion capture hardware experiments in a controlled environment.
Therefore, it is necessary to map the motion capture data to simulation models by employing a posture reconstruction algorithm.
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