Automatic Human Mocap Data Classification

作者：Kadu Harshad^*; Kuo C C Jay

来源：IEEE Transactions on Multimedia, 2014, 16(8): 2191-2202.

DOI：10.1109/TMM.2014.2360793

• Database management
• human motion analysis
• machine learning
• mocap data
• motion recognition
• n-fold cross validation
• suffix array
• SVM
• vector quantization

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.

• 出版日期2014-12