Transcript Three-dimensional Motion Capture, Modelling and Analysis

Three-dimensional
Motion Capture,
Modelling and
Analysis of
Ski Jumpers
Atle Nes
CSGSC 2005
Trondheim, April 28th
Overview
1. Project description
2. What kind of data are we interested in?
3. Capturing data:
Image acquisition, Camera system
4. Processing data:
Feature points, Motion capture, Photogrammetry
5. Interpreting data:
Visualization, Motion analysis
6. Conclusion
Project description
• Task: Design a computer
system that can capture
and study the motion of
ski jumpers in 3D.
• Goal: The results will be
used to give feedback to
the ski jumpers that can
help them to increase
their jumping lengths.
Data collection
• Will be gathered and analyzed in close
cooperation with Human Movement
Science Program at NTNU.
Data:
• Mainly from outdoor ski jumps captured at
Granåsen ski jumping hill here in
Trondheim.
• Also from indoor ski jumps captured at
Dragvoll sports facilities.
Granåsen ski jump arena
Image acquisition
• Video sequences are captured
simultanuously from multiple video
cameras.
Two decisive camera factors:
• Spatial resolution (pixels)
• Time resolution (frame rate)
Camera equipment
• 3 x AVT Marlin F080b
• IEEE1394 Firewire, DCAM
• 8-bit greyscale w/ max resolution
1024x768x15fps or 640x480x30fps
• Extra trigger cable/signal  Video capture
synchronization.
• Different camera lenses  Capture the
same area from different distances.
• Optical fibre  Extends the distance from
computer to cameras in the hill, keeping
the transmission speed.
Feature points
Robust feature points:
• Human body markers
(easy detectable)
• Naturally robust features
(more difficult).
• Want to have automatic
detection of robust
feature points using
simple image processing
techniques.
Motion capture
• Localizing, identifying and tracking
identical feature points in both
sequences of video images as well as
accross different camera views.
• Synchronized video streams ensures
good 3D coordinate accuracy.
Tracking w/ missing data
?
• Occluded features
 Redundancy using multiple
cameras with different views.
• Probability theory
 Guess the point position based on
feature point velocity.
• Another problem  Blur effect
Photogrammetry
• Matching corresponding feature
points from two or more cameras
allows us to calculate the exact
position of that feature point in 3D.
• Good camera placement is important
for good triangulation capabilities
(3D coordinate accuracy).
Camera calibration
• Coordinate system  On site
calibration using known coordinates in
the ski jumping arena.
• Direct Linear Transformation (DLT) by
Abdel-Aziz and Karara in 1971.
• Lens distortion (unlinear)
• Intelligent removal of the worst
calibration points (sources of error).
Visualization
• Feature point tracks are
connected back onto a
dynamic model of the ski
jumper.
• Dynamic model of ski
jumper is combined with
static model of ski jump
arena.
Motion analysis
• Done in close cooperation with
Human Movement Science Program
• Extract movements that have greatest
influence on the result.
• Using statistical tools and prior
knowledge about movements
• Project some movements to unseen
2D views.
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 2
Sports:
• Study top athletes for finding optimal
movement patterns.
Surveillance:
• Crowd surveillance and identification
of possible strange behaviour in a
shopping mall or airport.
Conclusion
• I have presented an overview of a
system that can capture, visualize
and analyze ski jumpers in a ski
jumping hill.
• Remains to see how well such a
system can perform and if it can help
the ski jumpers improve their skills.
Any questions?