Skeleton-Based Action Recognition using Citation-KNN on Bags of Time-Stamped Pose Descriptors

GÓMEZ FERNÁNDEZ, F.; UBALDE, S.; MEJAIL, M.

Phoenix, Arizona, USA

Conferencia; IEEE International Conference on Image Processing (ICIP); 2016

IEEE Signal Processing Society

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. Theproposed 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 or 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.