180 likes | 341 Views
Three-dimensional Motion Capture, Modelling and Analysis of Ski Jumpers. Atle Nes CSGSC 2005 Trondheim, April 28th. Overview. Project description What kind of data are we interested in? Capturing data: Image acquisition, Camera system
E N D
Three-dimensional Motion Capture, Modelling and Analysis of Ski Jumpers Atle NesCSGSC 2005 Trondheim, April 28th
Overview • Project description • What kind of data are we interested in? • Capturing data:Image acquisition, Camera system • Processing data:Feature points, Motion capture, Photogrammetry • Interpreting data:Visualization, Motion analysis • Conclusion
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. Project description
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.
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.
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. Feature points
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).
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. Visualization
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.