Affine moment invariants: a new tool for character recognition
Pattern Recognition Letters
SIGGRAPH '95 Proceedings of the 22nd annual conference on Computer graphics and interactive techniques
The visual analysis of human movement: a survey
Computer Vision and Image Understanding
Proceedings of the 27th annual conference on Computer graphics and interactive techniques
Robot Dynamics Algorithm
A Faster Method for Modeling Virtual Colony
VR '04 Proceedings of the IEEE Virtual Reality 2004
Adaptation of performed ballistic motion
ACM Transactions on Graphics (TOG)
Human motion simulation and action corpus
ICDHM'07 Proceedings of the 1st international conference on Digital human modeling
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There are mainly two Hi-Tech methods for athlete training. One method is based on virtual reality, where the athlete can learn and improve performance mainly through using virtual equipments to interact with the virtual environment. Another method is based on video analysis, where improvements can be made by comparing the videos of the trainees with those of excellent trainers. In this paper, we present a novel framework for athlete training, which can circumvent difficulties the current methods faced in practical applications. For retargeting the example motion to personalized virtual athlete, the coach interactively sets motion constraints with his experience based on motion warping and motion verification techniques. The display of the simulated motion is adjusted semi-automatically to create the reference virtual video with the same viewpoint as the real one. The moment invariants of both virtual and real athlete's silhouette are computed, and motion analysis result is presented subsequently. This method is more suitable for gymnastic athlete training because of without virtual equipment and more instructive having the same viewpoint in video analysis. Finally, an application of the proposed techniques to trampoline training is implemented.