Key Frames Detection in Motion Capture Recordings Using Machine Learning Approaches

The right choice of key frames is crucial for generation the valuable actions description for classification purposes. The machine learning approaches such as clustering of motion capture (MoCap) dataset can be used to calculate those key frames. K-means clustering already proved to be an efficient and effective method for key frames detection however to our knowledge no other clustering method has been used to this task. In this paper the several clustering methods namely model -based clustering with Gaussian Mixture model (Expectation -maximization algorithm), fuzzy clustering, K-medians clustering and hierarchical clustering are compared with K-means clustering in task of key frames detection. The comparison was done on dataset consisted of MoCap multimedia-quality recording of karate techniques that consisted of 12 different actions types (totally 480 actions samples). Results showed that the difference between clustering results increases while dataset is partitioned into more clusters. The second experiment was an evaluation what is an averaged percentage of recordings in training set and test set that contains all key frames detected by K-means algorithm. The proposed approach seems to have good generalization on the multimedia dataset when clusters number was set to two or three. The description of those two experiments is main novelty of this paper.