Evaluation of a skeleton-based method for human activity recognition on a large-scale RGB-D dataset

Abstract

Paper accepted for presentation at 2nd IET International Conference on Technologies for Active and Assisted Living (TechAAL), 24-25 October 2016, IET London: Savoy Place.Low cost RGB-D sensors have been used extensively in the field of Human Action Recognition. The availability of skeleton joints simplifies the process of feature extraction from depth or RGB frames, and this feature fostered the development of activity recognition algorithms using skeletons as input data. This work evaluates the performance of a skeleton-based algorithm for Human Action Recognition on a large-scale dataset. The algorithm exploits the bag of key poses method, where a sequence of skeleton features is represented as a set of key poses. A temporal pyramid is adopted to model the temporal structure of the key poses, represented using histograms. Finally, a multi-class SVM performs the classification task, obtaining promising results on the large-scale NTU RGB+D dataset.The authors would like to acknowledge the contribution of the COST Action IC1303 AAPELE (Architectures, Algorithms and Platforms for Enhanced Living Environments)