Skeleton-based action recognition using Citation-kNN on bags of time-stamped pose descriptors
Sebastián Ubalde, Francisco Gómez-Fernández, Norberto Adrián Goussies, Marta E. Mejail · 2016
With the advent of cost-effective depth sensors and the development of fast human-pose estimation algorithms, interest in action recognition from temporal skeleton sequences has been renewed. In this work we claim the task can be naturally seen as a Multiple Instance Learning (MIL) problem. Specifically, we model skeleton sequences as bags of time-stamped descriptors, and we present a new framework for action classification based on the Citation-kNN method. The proposed approach is effective in dealing with the large intra-class variability/inter-class similarity nature of the problem. Moreover, it is simple and provides a clear way for regulating tolerance to noise and temporal misalignment. Through extensive experiments on three datasets, we validate our approach and show that it compares favorably to other state-of-the-art skeleton-based action recognition methods.