Abstract
In this paper, we propose a computer vision scheme for action performance evaluation in video sequences. Different from action recognition algorithms that aim at the classification of an unknown action from a set of predefined reference actions, the action evaluation system scores the action performance of a trainee with respect to professional trainers. It can be applied to sports, exercise fitness and rehabilitation evaluation. The global spatiotemporal representation extended from the Motion History Image is used to describe space–time changes of an action in videos. A self-comparison mechanism that reconstructs the trainee’s action as a linear combination of the trainers’ temporal templates is used to measure the deviation. An exponential utility function converts the reconstruction error into an understandable score between 0% and 100%. Test results on table-tennis scenarios have shown the effectiveness of the proposed method. It is also computationally very fast for on-line, real-time feedback of action performance.
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