Recognition of predicted time series using chaotic and geometric features

The purpose of this project was to expand the applications of time series prediction and action recognition for use with motion capture data and football plays. Both the motion capture data and football play trajectories were represented in the form of multidimensional time series. Each point of interest on the human body or football players path, was represented in two or three time series, one for each dimension of motion recorded in the data. By formulating a phase space reconstruction of the data, the remainder of each input time series was predicted utilizing kernel regression. This process was applied to the first portion of a play. Utilizing features from the theory of chaotic systems and specialized geometric features, the specific type of motion for the motion capture data or the type of play for the football data was identified by using the features with various classifiers. The chaotic features used included the maximum Lyapunov exponent, the correlation integral, and the correlation dimension. The variance and mean were also utilized in conjunction with the chaotic features. The geometric features used were the minimum, maximum, mean, and median of the x, y, and z axis time series, as well as various angles and measures of the trajectory as a whole. The accuracy of the features and classifiers was compared and combinations of features were analyzed. The novelty of this work lies in the method to recognize types of actions from a prediction made from only a short, initial portion of an action utilizing various sets of features and classifiers