Radu Bogdan Rusu, Jan Bandouch, Franziska Meier, Irfan Essa and Michael Beetz (2009) “Human Action Recognition Using Global Point Feature Histograms and Action Shapes”, in Journal of Advanced Robotics, volume 23, pages 1873–1908, Koninklijke Brill NV, Leiden and The Robotics Society of Japan, 2009. [ DOI | PDF]
This paper investigates the recognition of human actions from three-dimensional (3-D) point clouds that encode the motions of people acting in sensor-distributed indoor environments. Data streams are time sequences of silhouettes extracted from cameras in the environment. From the 2-D silhouette contours we generate space–time streams by continuously aligning and stacking the contours along the time axis as third spatial dimension. The space–time stream of an observation sequence is segmented into parts corresponding to subactions using a pattern matching technique based on suffix trees and interval scheduling. Then, the segmented space–time shapes are processed by treating the shapes as 3-D point clouds and estimating global point feature histograms for them. The resultant models are clustered using statistical analysis and our experimental results indicate that the presented methods robustly derive different action classes. This holds despite large intra-class variance in the recorded datasets due to performances from different persons at different time intervals.
© Koninklijke Brill NV, Leiden and The Robotics Society of Japan, 2009
Keywords: Action recognition, point cloud, global features, action segmentation