Transcript Trondheim University College

Motion Capture of Ski
Jumpers in 3D
Trondheim University College
Faculty of informatics and e-learning
PhD student, Atle Nes
Bonn, 24-28th of October 2004
Trondheim, Norway (summer)
Trondheim, Norway (winter)
Main research areas
• Face recognition (master thesis)
• Human motion analysis (current)
Scenario: Ski jumpers
• Want to capture and
study the motion of ski
jumpers in 3D
• Results will be used to
give feedback to ski
jumpers that can help
them to increase their
jumping length
Granåsen ski jump
Capture video images
• Video sequences are captured
simultanuously from three video
cameras
• Results in large amounts of video
data (about 30 MByte/sec)
Our video cameras
•
•
•
•
AVT Marlin F080b (x3)
Digital IEEE1394 Firewire
8-bit greyscale
Resolution and frame rate:
1024x768x15fps or 640x480x30fps
Choose feature points
• Want to have automatic
detection of robust
feature points
• Robust feature points can
be human body markers
(easy detectable) or
naturally robust features
(more difficult)
Estimate 3D coordinates
• Matching corresponding feature points
from two or more cameras allows us to
calculate the exact position of that
feature point in 3D (photogrammetry).
• Cameras are placed such that the viewing
angles give good triangulation capabilities.
• Triangulation and video resolution
determines the accuracy.
Track features in time
• Cameras must have synchronized
their video streams to ensure good
3D coordinate accuracy when
tracking moving features.
• Feature localization problems with
blur when object (ski jumper) is
moving too fast compared to the
frame rate.
Connect features back
onto a 3D model
• Apply the feature motion tracks to a
dynamical model of a ski jumper.
• Be sure that all the movements made
by the ski jumper model are allowable
(cannot twist his head five times or
spin his leg through the other leg).
• Combine the ski jumper with a model
of the ski jumping stadium.
Visualize the combined
3D model
• A CAVE environment simulating a real
human view gives a much better view
than just viewing the model on a
regular PC screen.
• The mobility of the Immersion
Square is very nice.
Analyse motion
• Using statistical tools
• Prior knowledge about movements
• Project certain movements to 2D
Related applications
• Medical:
- Diagnosis of infant spontaneous
movements for early detection of
possible brain damage (cerebral
palsy).
- Diagnosis of adult movements (walk),
for determination of cause of
problems.
Related applications
• Sports:
- Study top athletes for finding
optimal movement patterns.
Surveillance:
- Crowd surveillance and identification
of possible strange behaviour in a
shopping mall or airport.
Any questions?