1. 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

2. Trondheim, Norway (summer)

3. Trondheim, Norway (winter)

4. Main research areas Face recognition (master thesis) Human motion analysis (current)

5. 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

6. Granåsen ski jump

7. Capture video images Video sequences are captured simultanuously from three video cameras Results in large amounts of video data (about 30 MByte/sec)

8. Our video cameras AVT Marlin F080b (x3) Digital IEEE1394 Firewire 8-bit greyscale Resolution and frame rate: 1024x768x15fps or 640x480x30fps

9. 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)

10. 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.

11. 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.

12. 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.

13. 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.

14. Analyse motion Using statistical tools Prior knowledge about movements Project certain movements to 2D

15. 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.

16. 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.

17. Any questions?