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3d Pose Detection. Used by Kinect Accurate when the pose closely matches a stored pose Inaccurate when novel poses are made Can often produce shaky movement due to pose snapping. 3d Pose Tracking. Calculate poses based on previous poses and current data No datasets required - PowerPoint PPT Presentation
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3d Pose Detection• Used by Kinect• Accurate when the pose closely matches a stored pose• Inaccurate when novel poses are made• Can often produce shaky movement due to pose snapping
3d Pose Tracking• Calculate poses based on previous poses and current data• No datasets required• Has issues with local minima
Hybrid
• Store a collection of preprocessed poses• Start session with a detected pose• Track until a failure is hit• Detect that frame, and continue
tracking from there
Pose Tracking in Detail• Given the current set of pixels, and the calculated poses of the past
few frames, calculate the most probable current pose• Repeat the following until error is reduced• Calculate a hypothesized 3d representation (rendered image) using the 3d
camera image (observed data)• Calculate quite a lot about the rendered image:
• Edepth, Eextra, Esilhouette, Eprior
• Adjust the hypothesized pose accordingly• If that fails significantly, “detect” the current pose and try again
Optimizing the Position• Solve energy minimization problems:• Edepth: Depth data, excluding any rendered foreground pixels that match
background observed pixels• Eextra: The above depth term, but with added calculations where rendered
pixels don’t match with observed pixels• Esilhouette: The difference in silhouettes (pixel is in foreground of one image but
the background of the other)• Eprior: Evaluation of the rendered pose based on the previous rendered poses
More Pose Detecting• Grab a random subset of pixels and match them to known poses• Throw out uncertain data• 20k poses total
Setup• User must stand in a specific pose for calibration• Select random subset of pixels to determine widths of limbs
Results
Results• Roughly 1 in 200 frames
reinitialized by the detector• Very fast movement often
causes local minima• On average, 1 failure every
33 seconds• High performance running in
parallel on the GPU
http://www.youtube.com/watch?v=ICFKEOk3SyA&feature=youtu.be
Further Work• Use skinned mesh models• Increase pose set• Use color data in the
algorithm as well