Action recognition on motion capture data using a dynemes and forward differences representation

• We propose a new method for action/movement recognition on motion capture data.
• The method involves data representation and a bag of words framework.
• We use a novel modified K-means clustering algorithm suitable for angular data.
• Our method provides very good results in a number of challenging datasets.

In this paper we introduce a novel method for action/movement recognition in motion capture data. The joints orientation angles and the forward differences of these angles in different temporal scales are used to represent a motion capture sequence. Initially K-means is applied on training data to discover the most representative patterns on orientation angles and their forward differences. A novel K-means variant that takes into account the periodic nature of angular data is applied on the former. Each frame is then assigned to one or more of these patterns and histograms that describe the frequency of occurrence of these patterns for each movement are constructed. Nearest neighbour and SVM classification are used for action recognition on the test data. The effectiveness and robustness of this method is shown through extensive experimental results on four standard databases of motion capture data and various experimental setups.