Tree based regression methods for gap reconstruction of motion capture sequences

In the article, we study application of tree based regression methods for reconstruction of gaps in marker trajectories in optical motion capture systems. Having perfect ground truth sequences, we artificially introduced gaps of lengths between 0.1 and 2 s, then reconstructed them using various tree based regression methods – CART, LS boost, bagging, random forests, M5P (model tree). We also tested several methods known from the literature – neural networks (NN), interpolation, and matrix completion. We found out that in general, tree based methods perform poorly, but M5P model tree performs very well, especially for longer gaps, with results on average on par or better than neural networks. Finally, we compared the outcomes of M5P and NN, they appear to be uncorrelated, so they offer the possibility to create an ensemble predictor based on both methods.