Motion Models for Monocular 3–D Human Body Tracking
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We investigated the use of motion models based on either Principal Component Analysis or Gaussian Processes to formulate the monocular tracking problem as one of minimizing differentiable objective functions. As shown in the results below, we can handle cyclic motions, such as walking and running, as well as acyclical ones, such as a golf swing.
Furthermore, by detecting key postures, we can achieve robust and fully automated 3D motion recovery of people seen from arbitrary viewpoints by a single and potentially moving camera.
Golf Swing #1 [top]
By tracking the feet, knees, hands, and head of the golfer in the video sequence and fitting a parameterized model of a golf swing to the resulting trajectories, we can recover the 3D motion of the skeleton.
On the left we see the 3D skeleton reprojected onto the original video sequence and on the right a 3D avatar that performs the same motion as the real golfer. The hands were tracked using a method we developed in earlier work.
Golf Swing #2 [top]
The same procedure is applied to a long swing, as opposed to the short swing shown above.
Pedestrian Seen from Arbitrary Viewpoints [top]
The algorithm automatically detects the pedestrian at the moment of the walking cycle when his legs are furthest apart and fits a walking motion model in between detections. As a result the subject can be tracked from an arbitrary viewpoint, his full 3D motion recovered, and his instantaneous speed estimated.
No code available.