Automatic Human Mocap Data Classification

Abstract

Automatic classification of human motion capture (mocap) data has many commercial, biomechanical, and medical applications and is the principal focus of this paper. First, we propose a multi-resolution string representation scheme based on the tree-structured vector quantization (TSVQ) to transform the time-series of human poses into codeword sequences. Then, we take the temporal variations of human poses into account via codeword sequence matching. Furthermore, we develop a family of pose-histogram-based classifiers to examine the spatial distribution of human poses. We analyze the performance of the temporal and spatial classifiers separately. To achieve a higher classification rate, we merge their decisions and soft scores using novel fusion methods. The proposed fusion solutions are tested on a wide variety of sequences from the CMU mocap database using five-fold cross validation, and a correct classification rate of 99.6% is achieved.