Clustering poses of motion capture data using limb centroids

With increasing popularity of using motion capture hardware for motion synthesis, studies that exploit large motion databases for faster indexing and retrieval become more important. Here, an automated procedure to analyze motion databases and cluster poses according to various feature vectors is presented. We propose the use of limb centroids as an alternative feature vector. Limb centroids have a smaller size compared to standard alternatives, thus time complexity is reduced while satisfactory clusters are achieved when used with our iterative clustering procedure. In addition, along with two different metrics to evaluate formed clusters, we also propose pose clouds for visualization and perceptual analysis.