Probabilistic classification of skeleton sequences

Abstract

Automatic classification of 3D skeleton sequences of human motions has applications in many domains, ranging from entertainment to medicine. The classification is a difficult problem as the motions belonging to the same class needn’t be well segmented and can be performed by subjects of various body sizes in different styles and speeds. The state-of-the-art recognition approaches commonly solve this problem by training recurrent neural networks to learn the contextual dependency in both spatial and temporal domains. In this paper, we employ a distance-based similarity measure, based on deep convolutional features, to search for the k-nearest motions with respect to a query motion being classified. The retrieved neighbors are analyzed and re-ranked by additional measures that are automatically chosen for individual queries. The combination of deep features, dynamism in the similarity-measure selection, and a new kNN classifier brings the highest classification accuracy on a challenging dataset with 130 classes. Moreover, the proposed approach can promptly react to changing training data without any need for a retraining process.