MENU: Home Bio Affiliations Research Teaching Publications Videos Collaborators/Students Contact FAQ ©2007-14 RSS

Paper in Advanced Robotics (2009): “Human Action Recognition Using Global Point Feature Histograms and Action Shapes”

October 29th, 2009 Irfan Essa Posted in Activity Recognition, Franzi Meier, Intelligent Environments, Michael Beetz, Papers No Comments »

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

Overview of the approach.

Overview of the approach.

Keywords: Action recognition, point cloud, global features, action segmentation

AddThis Social Bookmark Button

Paper: ICPR (2008) “3D Shape Context and Distance Transform for Action Recognition”

December 8th, 2008 Irfan Essa Posted in Activity Recognition, Aware Home, Face and Gesture, Franzi Meier, Matthias Grundmann, PAMI/ICCV/CVPR/ECCV, Papers 1 Comment »

M. Grundmann, F. Meier, and I. Essa (2008) “3D Shape Context and Distance Transform for Action Recognition”, In Proceedings of International Conference on Pattern Recognition (ICPR) 2008, Tampa, FL. [Project Page | DOI | PDF]


We propose the use of 3D (2D+time) Shape Context to recognize the spatial and temporal details inherent in human actions. We represent an action in a video sequence by a 3D point cloud extracted by sampling 2D silhouettes over time. A non-uniform sampling method is introduced that gives preference to fast moving body parts using a Euclidean 3D Distance Transform. Actions are then classified by matching the extracted point clouds. Our proposed approach is based on a global matching and does not require specific training to learn the model. We test the approach thoroughly on two publicly available datasets and compare to several state-of-the-art methods. The achieved classification accuracy is on par with or superior to the best results reported to date.

AddThis Social Bookmark Button