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Rowing Motion Capture SystemSimon Fothergill
Ph.D. student, Digital Technology Group, Computer Laboratory
Jesus College graduate conference May 2009
Overview
• The Bigger Picture
• Previous work
• Problem
• Process
• Data Capture System
• Results
• Future work
The Bigger Picture
• Sentient Computing!
• Computer Vision
• Pattern Recognition & Machine learning
• A long way to go!
The Bigger Picture – Watching Humans
• Physical Performances
• Heath care
• What are they doing?
• How well are they doing it?
• How should be improved?
• How should they be told?
Previous Work - Activity / Gesture recognition
• Motion capture methods have included:
• Blob tracking
• Point trajectories
• Recognition techniques have included:
• Single frame
• Multiple frame
• Parametric
Learn the quality of a performance from body part trajectories
• Minimise markers using redundancy
• Complex trajectories, continuous score
• Flexible rubrics require learning
• Different types of expert labelling:
• Explanations
• Non-specific / specific
• Different granularities of quality
• Which sections of the trajectory are how relevant?
• One section of a can depend on many aspects
Process
Learning
Judging
Performance
Capture motion
Expert coach labels with their judgement
Trajectories
Inference modelLearn
Video
Performance
Capture motion
Trajectories Inference model
FeaturesExtract and select features
Features
Extract and select features
Judgement
Evaluate
Capture video
Data Capture System - Architecture
Nintendo Wii controller
Bluetooth
IR 1024x768 camera(100Hz)
Nintendo Wii controller
IR 1024x768 camera
(100Hz)
PC
Wii libraryBluetooth library
C server
Bluetooth
PCJava / C client
Video camera(30Hz)
Fire wire
TCP/IP
C server
Buffer Wii controller Wii controller
Data Capture System – Calibration and operation
Server
Triangulation
Stereo calibration
Client
4 x 2D coordinates
4 x 3D coordinates
Erg calibration
Label markers
Transform to ECS
Update ECS if necessary ECS
Detect strokes
Log data Log files
Save picture
Encodes video
Calibrate labeller
Calibrate WMCS
StorageBatch
Display on GUI
Calculate stats
Control camera
Cal
ibra
tion
Live
ope
ratio
n
Preliminary Results
• Preliminary results have been obtained using a dataset of 6 rowers and the complete trajectory of the erg handle only. Binary classification over stroke quality was done using tempo-spatial features of the trajectory and a neural network. Two training methods were compared.
60 70 80 90 100
Quick hands (2)
Early open back (2)
Separate arms/legs (3)
Overreaching (4)
Percent of correctly classified strokes
Gradient descent training
Moore-Penrose training
Classification accuracy across given number of performers, for quality of individual aspects
of technique.
Summary and Further Work
• Data capture system and how it fits into the bigger picture
• More information is available on the feature extraction & selection and inference algorithms.
• A larger data set would allow conclusive results to be obtained
• Feature extraction and selection methods that address using the relevant segments of the relevant trajectories
• More sophisticated modelling based on particle filters
• Supports multiple body parts and labelling methods
• Uses a distribution of motion vectors to probabilistically track the “quality so far” as the stroke evolves.
In Conclusion
• Advertisement!
• Acknowledgements
• Professor Andy Hopper, Dr Sean Holden, Dr Robert Harle
• Members of the DTG and Rainbow groups, Computer Laboratory
• Jesus College, JCBC and the Graduate society
• References
• Optical tracking using commodity hardware, Hay, S.; Newman, J.; Harle, R.; ISMAR 2008. Page(s):159 - 160
Thank you!
Questions?
Please come down to the boathouse and use the data capture system!